



"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers.models.gptj.modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    shape_list,
		)





class   UpperCamelCase	:




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_   ):
			_lowercase				: Dict     =   parent
			_lowercase				: List[Any]     =   13
			_lowercase				: Any     =   7
			_lowercase				: List[str]     =   True
			_lowercase				: Dict     =   True
			_lowercase				: Optional[Any]     =   True
			_lowercase				: Union[str, Any]     =   True
			_lowercase				: List[Any]     =   True
			_lowercase				: List[str]     =   99
			_lowercase				: Any     =   32
			_lowercase				: List[Any]     =   4
			_lowercase				: Optional[Any]     =   2
			_lowercase				: List[Any]     =   4
			_lowercase				: Dict     =   37
			_lowercase				: Dict     =   """gelu"""
			_lowercase				: Tuple     =   0.1
			_lowercase				: str     =   0.1
			_lowercase				: int     =   5_12
			_lowercase				: int     =   16
			_lowercase				: List[Any]     =   2
			_lowercase				: List[Any]     =   0.02
			_lowercase				: List[Any]     =   3
			_lowercase				: Optional[Any]     =   4
			_lowercase				: Union[str, Any]     =   None
			_lowercase				: List[str]     =   self.vocab_size - 1
			_lowercase				: List[Any]     =   self.vocab_size - 1
			_lowercase				: int     =   self.vocab_size - 1






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[Any]     =   ids_tensor([self.batch_size, self.seq_length]				,self.vocab_size   )

			_lowercase				: Any     =   None
			if self.use_input_mask:
					_lowercase				: Any     =   random_attention_mask([self.batch_size, self.seq_length]   )

			_lowercase				: Union[str, Any]     =   None
			if self.use_token_type_ids:
					_lowercase				: Any     =   ids_tensor([self.batch_size, self.seq_length]				,self.type_vocab_size   )

			_lowercase				: List[str]     =   None
			if self.use_mc_token_ids:
					_lowercase				: Tuple     =   ids_tensor([self.batch_size, self.num_choices]				,self.seq_length   )

			_lowercase				: Optional[Any]     =   None
			_lowercase				: List[Any]     =   None
			_lowercase				: Optional[int]     =   None
			if self.use_labels:
					_lowercase				: Union[str, Any]     =   ids_tensor([self.batch_size]				,self.type_sequence_label_size   )
					_lowercase				: int     =   ids_tensor([self.batch_size, self.seq_length]				,self.num_labels   )
					_lowercase				: Optional[Any]     =   ids_tensor([self.batch_size]				,self.num_choices   )

			_lowercase				: Optional[int]     =   GPTJConfig(
			    vocab_size=self.vocab_size				,n_embd=self.hidden_size				,n_layer=self.num_hidden_layers				,n_head=self.num_attention_heads				,intermediate_size=self.intermediate_size				,hidden_act=self.hidden_act				,hidden_dropout_prob=self.hidden_dropout_prob				,attention_probs_dropout_prob=self.attention_probs_dropout_prob				,n_positions=self.max_position_embeddings				,type_vocab_size=self.type_vocab_size				,initializer_range=self.initializer_range				,bos_token_id=self.bos_token_id				,eos_token_id=self.eos_token_id				,pad_token_id=self.pad_token_id				,rotary_dim=self.rotary_dim				,return_dict=UpperCAmelCase_				,)

			_lowercase				: Optional[Any]     =   ids_tensor([self.num_hidden_layers, self.num_attention_heads]				,2   )

			return (
			    config,
			    input_ids,
			    input_mask,
			    head_mask,
			    token_type_ids,
			    mc_token_ids,
			    sequence_labels,
			    token_labels,
			    choice_labels,
			)






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,*UpperCAmelCase_   ):
			_lowercase				: Union[str, Any]     =   TFGPTJModel(config=UpperCAmelCase_   )
			_lowercase				: str     =   {
			    """input_ids""": input_ids,
			    """attention_mask""": input_mask,
			    """token_type_ids""": token_type_ids,
			}
			_lowercase				: Optional[int]     =   model(UpperCAmelCase_   )

			_lowercase				: Tuple     =   [input_ids, None, input_mask]  # None is the input for 'past'
			_lowercase				: List[str]     =   model(UpperCAmelCase_   )

			_lowercase				: Tuple     =   model(UpperCAmelCase_   )

			self.parent.assertEqual(result.last_hidden_state.shape				,(self.batch_size, self.seq_length, self.hidden_size)   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,*UpperCAmelCase_   ):
			_lowercase				: Optional[int]     =   TFGPTJModel(config=UpperCAmelCase_   )

			# first forward pass
			_lowercase				: Optional[int]     =   model(UpperCAmelCase_				,token_type_ids=UpperCAmelCase_				,use_cache=UpperCAmelCase_   )
			_lowercase				: Optional[Any]     =   model(UpperCAmelCase_				,token_type_ids=UpperCAmelCase_   )
			_lowercase				: Union[str, Any]     =   model(UpperCAmelCase_				,token_type_ids=UpperCAmelCase_				,use_cache=UpperCAmelCase_   )

			self.parent.assertTrue(len(UpperCAmelCase_   ) == len(UpperCAmelCase_   )   )
			self.parent.assertTrue(len(UpperCAmelCase_   ) == len(UpperCAmelCase_   ) + 1   )

			_lowercase     ,  _lowercase				: List[Any]     =   outputs.to_tuple()

			# create hypothetical next token and extent to next_input_ids
			_lowercase				: List[Any]     =   ids_tensor((self.batch_size, 1)				,config.vocab_size   )
			_lowercase				: Tuple     =   ids_tensor([self.batch_size, 1]				,self.type_vocab_size   )

			# append to next input_ids and token_type_ids
			_lowercase				: List[str]     =   tf.concat([input_ids, next_tokens]				,axis=-1   )
			_lowercase				: Any     =   tf.concat([token_type_ids, next_token_types]				,axis=-1   )

			_lowercase				: int     =   model(UpperCAmelCase_				,token_type_ids=UpperCAmelCase_   )["""last_hidden_state"""]
			_lowercase				: Any     =   model(UpperCAmelCase_				,token_type_ids=UpperCAmelCase_				,past_key_values=UpperCAmelCase_   )[
			    """last_hidden_state"""
			]

			# select random slice
			_lowercase				: int     =   int(ids_tensor((1,)				,shape_list(UpperCAmelCase_   )[-1]   )   )
			_lowercase				: int     =   output_from_no_past[:, -1, random_slice_idx]
			_lowercase				: Optional[int]     =   output_from_past[:, 0, random_slice_idx]

			# test that outputs are equal for slice
			tf.debugging.assert_near(UpperCAmelCase_				,UpperCAmelCase_				,rtol=1E-6   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,*UpperCAmelCase_   ):
			_lowercase				: int     =   TFGPTJModel(config=UpperCAmelCase_   )

			# create attention mask
			_lowercase				: List[str]     =   self.seq_length // 2
			_lowercase				: List[str]     =   tf.ones((self.batch_size, half_seq_length)				,dtype=tf.intaa   )
			_lowercase				: Dict     =   tf.zeros((self.batch_size, self.seq_length - half_seq_length)				,dtype=tf.intaa   )
			_lowercase				: Optional[int]     =   tf.concat([attn_mask_begin, attn_mask_end]				,axis=1   )

			# first forward pass
			_lowercase     ,  _lowercase				: Optional[Any]     =   model(UpperCAmelCase_				,attention_mask=UpperCAmelCase_   ).to_tuple()

			# create hypothetical next token and extent to next_input_ids
			_lowercase				: str     =   ids_tensor((self.batch_size, 1)				,config.vocab_size   )

			# change a random masked slice from input_ids
			_lowercase				: Union[str, Any]     =   ids_tensor((1,)				,UpperCAmelCase_   ).numpy() + 1
			_lowercase				: int     =   ids_tensor((self.batch_size, self.seq_length)				,config.vocab_size   )
			_lowercase				: Dict     =   tf.range(self.seq_length   ) == (self.seq_length - random_seq_idx_to_change)
			_lowercase				: Optional[Any]     =   tf.transpose(
			    tf.broadcast_to(tf.expand_dims(UpperCAmelCase_				,-1   )				,(self.seq_length, self.batch_size)   )   )
			_lowercase				: Any     =   tf.where(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   )

			# append to next input_ids and attn_mask
			_lowercase				: List[Any]     =   tf.concat([input_ids, next_tokens]				,axis=-1   )
			_lowercase				: List[str]     =   tf.concat([attn_mask, tf.ones((shape_list(UpperCAmelCase_   )[0], 1)				,dtype=tf.intaa   )]				,axis=1   )

			# get two different outputs
			_lowercase				: Optional[Any]     =   model(UpperCAmelCase_				,attention_mask=UpperCAmelCase_   )["""last_hidden_state"""]
			_lowercase				: Optional[Any]     =   model(UpperCAmelCase_				,past_key_values=UpperCAmelCase_				,attention_mask=UpperCAmelCase_   )[
			    """last_hidden_state"""
			]

			# select random slice
			_lowercase				: int     =   int(ids_tensor((1,)				,shape_list(UpperCAmelCase_   )[-1]   )   )
			_lowercase				: List[str]     =   output_from_no_past[:, -1, random_slice_idx]
			_lowercase				: Optional[Any]     =   output_from_past[:, 0, random_slice_idx]

			# test that outputs are equal for slice
			tf.debugging.assert_near(UpperCAmelCase_				,UpperCAmelCase_				,rtol=1E-12   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,*UpperCAmelCase_   ):
			_lowercase				: Union[str, Any]     =   TFGPTJModel(config=UpperCAmelCase_   )

			_lowercase				: str     =   input_ids[:1, :]
			_lowercase				: List[Any]     =   input_mask[:1, :]
			_lowercase				: List[Any]     =   token_type_ids[:1, :]
			_lowercase				: Optional[Any]     =   1

			# first forward pass
			_lowercase				: Optional[int]     =   model(UpperCAmelCase_				,attention_mask=UpperCAmelCase_				,token_type_ids=UpperCAmelCase_				,use_cache=UpperCAmelCase_   )

			_lowercase     ,  _lowercase				: List[Any]     =   outputs.to_tuple()

			# create hypothetical next token and extent to next_input_ids
			_lowercase				: Optional[int]     =   ids_tensor((self.batch_size, 3)				,config.vocab_size   )
			_lowercase				: List[Any]     =   ids_tensor((self.batch_size, 3)				,2   )
			_lowercase				: Tuple     =   ids_tensor((self.batch_size, 3)				,self.type_vocab_size   )

			# append to next input_ids and token_type_ids
			_lowercase				: List[str]     =   tf.concat([input_ids, next_tokens]				,axis=-1   )
			_lowercase				: Any     =   tf.concat([input_mask, next_attn_mask]				,axis=-1   )
			_lowercase				: int     =   tf.concat([token_type_ids, next_token_types]				,axis=-1   )

			_lowercase				: Union[str, Any]     =   model(
			    UpperCAmelCase_				,token_type_ids=UpperCAmelCase_				,attention_mask=UpperCAmelCase_   )["""last_hidden_state"""]
			_lowercase				: List[str]     =   model(
			    UpperCAmelCase_				,token_type_ids=UpperCAmelCase_				,attention_mask=UpperCAmelCase_				,past_key_values=UpperCAmelCase_				,)["""last_hidden_state"""]
			self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]   )

			# select random slice
			_lowercase				: int     =   int(ids_tensor((1,)				,shape_list(UpperCAmelCase_   )[-1]   )   )
			_lowercase				: Union[str, Any]     =   output_from_no_past[:, -3:, random_slice_idx]
			_lowercase				: Dict     =   output_from_past[:, :, random_slice_idx]

			# test that outputs are equal for slice
			tf.debugging.assert_near(UpperCAmelCase_				,UpperCAmelCase_				,rtol=1E-3   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,*UpperCAmelCase_   ):
			_lowercase				: Tuple     =   TFGPTJForCausalLM(config=UpperCAmelCase_   )
			_lowercase				: Tuple     =   {
			    """input_ids""": input_ids,
			    """attention_mask""": input_mask,
			    """token_type_ids""": token_type_ids,
			}
			_lowercase				: List[str]     =   model(UpperCAmelCase_   )
			self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.seq_length, self.vocab_size)   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[int]     =   self.prepare_config_and_inputs()

			(
			    (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  
			)				: Optional[int]     =   config_and_inputs

			_lowercase				: Union[str, Any]     =   {
			    """input_ids""": input_ids,
			    """token_type_ids""": token_type_ids,
			    """attention_mask""": input_mask,
			}
			return config, inputs_dict







@require_tf
class   UpperCamelCase	(     snake_case					,   snake_case					,   snake_case					,   unittest.TestCase       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       int						       =  (
	    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
	    if is_tf_available()
	    else ()
	)

	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  (TFGPTJForCausalLM,) if is_tf_available() else ()
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  (
	    {
	        "feature-extraction": TFGPTJModel,
	        "question-answering": TFGPTJForQuestionAnswering,
	        "text-classification": TFGPTJForSequenceClassification,
	        "text-generation": TFGPTJForCausalLM,
	        "zero-shot": TFGPTJForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	SCREAMING_SNAKE_CASE_							:       str						       =  False
	SCREAMING_SNAKE_CASE_							:       str						       =  False
	SCREAMING_SNAKE_CASE_							:       List[str]						       =  False
	SCREAMING_SNAKE_CASE_							:       Any						       =  False






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
			if (
			    pipeline_test_casse_name == "QAPipelineTests"
			    and tokenizer_name is not None
			    and not tokenizer_name.endswith("""Fast"""   )
			):
					# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
					# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
					# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
					return True

			return False






	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   TFGPTJModelTester(self   )
			_lowercase				: List[str]     =   ConfigTester(self				,config_class=UpperCAmelCase_				,n_embd=37   )






	def    lowerCamelCase__					(     self   ):
			self.config_tester.run_common_tests()






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[Any]     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_gptj_model(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Dict     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_gptj_model_past(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[Any]     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_gptj_model_attention_mask_past(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Any     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_gptj_model_past_large_inputs(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Dict     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_gptj_lm_head_model(*UpperCAmelCase_   )






	@slow
	@unittest.skipIf(
	    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""   )   ) > 0				,"""skip testing on GPU for now to avoid GPU OOM."""				,)
	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   TFGPTJModel.from_pretrained("""EleutherAI/gpt-j-6B"""				,from_pt=UpperCAmelCase_   )
			self.assertIsNotNone(UpperCAmelCase_   )






	@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""   )
	def    lowerCamelCase__					(     self   ):
			super().test_resize_token_embeddings()







@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class   UpperCamelCase	(     unittest.TestCase       ):




	"""simple docstring"""






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Dict     =   TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""				,from_pt=UpperCAmelCase_   )
			_lowercase				: List[str]     =   tf.convert_to_tensor([[4_64, 32_90]]				,dtype=tf.intaa   )  # The dog
			# fmt: off
			# The dog is a man's best friend. It is a loyal companion, and it is a friend
			_lowercase				: Union[str, Any]     =   [4_64, 32_90, 3_18, 2_57, 5_82, 3_38, 12_66, 15_45, 13, 6_32, 3_18, 2_57, 91_12, 1_51_85, 11, 2_90, 3_40, 3_18, 2_57, 15_45]
			# fmt: on
			_lowercase				: List[str]     =   model.generate(UpperCAmelCase_				,do_sample=UpperCAmelCase_   )
			self.assertListEqual(output_ids[0].numpy().tolist()				,UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[Any]     =   AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""				,revision="""float16"""   )
			_lowercase				: int     =   TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""				,revision="""float16"""				,from_pt=UpperCAmelCase_   )

			_lowercase				: str     =   tokenizer("""Today is a nice day and"""				,return_tensors="""tf"""   )
			# forces the generation to happen on CPU, to avoid GPU-related quirks
			with tf.device(""":/CPU:0"""   ):
					_lowercase				: List[Any]     =   model.generate(**UpperCAmelCase_				,do_sample=UpperCAmelCase_				,seed=[42, 0]   )
			_lowercase				: Any     =   tokenizer.decode(output_ids[0]				,skip_special_tokens=UpperCAmelCase_   )

			_lowercase				: Union[str, Any]     =   """Today is a nice day and I’m going to go for a walk. I’"""
			self.assertEqual(UpperCAmelCase_				,UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[Any]     =   TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""				,revision="""float16"""				,from_pt=UpperCAmelCase_   )
			_lowercase				: int     =   AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""				,revision="""float16"""   )

			_lowercase				: List[Any]     =   """left"""

			# Define PAD Token = EOS Token = 50256
			_lowercase				: List[str]     =   tokenizer.eos_token
			_lowercase				: Dict     =   model.config.eos_token_id

			# use different length sentences to test batching
			_lowercase				: List[str]     =   [
			    """Hello, my dog is a little""",
			    """Today, I""",
			]
			_lowercase				: List[str]     =   [
			    """Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia""",
			    """Today, I’m going to be talking about a topic that’""",
			]
			return model, tokenizer, sentences, expected_output_sentences






	def    lowerCamelCase__					(     self   ):
			# Confirms that we get the expected results with left-padded beam search
			_lowercase     ,  _lowercase     ,  _lowercase     ,  _lowercase				: List[str]     =   self._get_beam_search_test_objects()

			_lowercase				: List[str]     =   tokenizer(UpperCAmelCase_				,return_tensors="""tf"""				,padding=UpperCAmelCase_   )
			_lowercase				: List[str]     =   model.generate(**UpperCAmelCase_				,do_sample=UpperCAmelCase_				,num_beams=2   )
			_lowercase				: List[str]     =   tokenizer.batch_decode(UpperCAmelCase_				,skip_special_tokens=UpperCAmelCase_   )
			self.assertListEqual(UpperCAmelCase_				,UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			# Confirms that left-padding is working properly
			_lowercase     ,  _lowercase     ,  _lowercase     ,  _lowercase				: List[str]     =   self._get_beam_search_test_objects()

			_lowercase				: str     =   tokenizer(UpperCAmelCase_				,return_tensors="""tf"""				,padding=UpperCAmelCase_   )
			_lowercase				: Tuple     =   tokenizer(sentences[0]				,return_tensors="""tf"""   )
			_lowercase				: Optional[int]     =   model.generate(**UpperCAmelCase_				,do_sample=UpperCAmelCase_				,num_beams=2   )
			_lowercase				: Tuple     =   (
			    shape_list(inputs_non_padded["""input_ids"""]   )[-1]
			    - tf.reduce_sum(tf.cast(inputs["""attention_mask"""][-1]				,tf.intaa   )   ).numpy()
			)
			_lowercase				: Optional[Any]     =   tokenizer(sentences[1]				,return_tensors="""tf"""   )
			_lowercase				: Dict     =   model.generate(
			    **UpperCAmelCase_				,do_sample=UpperCAmelCase_				,num_beams=2				,max_length=model.config.max_length - num_paddings   )
			_lowercase				: Optional[Any]     =   tokenizer.decode(output_non_padded[0]				,skip_special_tokens=UpperCAmelCase_   )
			_lowercase				: Dict     =   tokenizer.decode(output_padded[0]				,skip_special_tokens=UpperCAmelCase_   )
			self.assertListEqual(UpperCAmelCase_				,[non_padded_sentence, padded_sentence]   )






	def    lowerCamelCase__					(     self   ):
			# Confirms that XLA is working properly
			_lowercase     ,  _lowercase     ,  _lowercase     ,  _lowercase				: List[Any]     =   self._get_beam_search_test_objects()

			_lowercase				: Optional[int]     =   tokenizer(UpperCAmelCase_				,return_tensors="""tf"""				,padding=UpperCAmelCase_   )
			_lowercase				: Tuple     =   tf.function(model.generate				,jit_compile=UpperCAmelCase_   )
			_lowercase				: str     =   xla_generate(**UpperCAmelCase_				,do_sample=UpperCAmelCase_				,num_beams=2   )
			_lowercase				: List[str]     =   tokenizer.batch_decode(UpperCAmelCase_				,skip_special_tokens=UpperCAmelCase_   )
			self.assertListEqual(UpperCAmelCase_				,UpperCAmelCase_   )








"""simple docstring"""







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		UpperCAmelCase:  List[str]			     =				"""platform"""

		import jax
		import jax.numpy as jnp

		from transformers import MarianTokenizer
		from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase=None   ,				__UpperCAmelCase=None   ,				__UpperCAmelCase=None   ,				__UpperCAmelCase=None   ,				__UpperCAmelCase=None   ,				__UpperCAmelCase=None   ,				):
		if attention_mask is None:
				_lowercase				: Union[str, Any]     =   np.where(input_ids != config.pad_token_id   ,				1   ,				0      )
		if decoder_attention_mask is None:
				_lowercase				: Dict     =   np.where(decoder_input_ids != config.pad_token_id   ,				1   ,				0      )
		if head_mask is None:
				_lowercase				: Any     =   np.ones((config.encoder_layers, config.encoder_attention_heads)      )
		if decoder_head_mask is None:
				_lowercase				: Union[str, Any]     =   np.ones((config.decoder_layers, config.decoder_attention_heads)      )
		if cross_attn_head_mask is None:
				_lowercase				: Optional[int]     =   np.ones((config.decoder_layers, config.decoder_attention_heads)      )
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": attention_mask,
		}





class   UpperCamelCase	:




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_				,UpperCAmelCase_=13				,UpperCAmelCase_=7				,UpperCAmelCase_=True				,UpperCAmelCase_=False				,UpperCAmelCase_=99				,UpperCAmelCase_=16				,UpperCAmelCase_=2				,UpperCAmelCase_=4				,UpperCAmelCase_=4				,UpperCAmelCase_="gelu"				,UpperCAmelCase_=0.1				,UpperCAmelCase_=0.1				,UpperCAmelCase_=32				,UpperCAmelCase_=2				,UpperCAmelCase_=1				,UpperCAmelCase_=0				,UpperCAmelCase_=0.02				,):
			_lowercase				: List[Any]     =   parent
			_lowercase				: str     =   batch_size
			_lowercase				: Tuple     =   seq_length
			_lowercase				: Optional[Any]     =   is_training
			_lowercase				: Optional[Any]     =   use_labels
			_lowercase				: Optional[int]     =   vocab_size
			_lowercase				: List[Any]     =   hidden_size
			_lowercase				: Dict     =   num_hidden_layers
			_lowercase				: Optional[Any]     =   num_attention_heads
			_lowercase				: Optional[Any]     =   intermediate_size
			_lowercase				: Tuple     =   hidden_act
			_lowercase				: Tuple     =   hidden_dropout_prob
			_lowercase				: Tuple     =   attention_probs_dropout_prob
			_lowercase				: Union[str, Any]     =   max_position_embeddings
			_lowercase				: Any     =   eos_token_id
			_lowercase				: Optional[Any]     =   pad_token_id
			_lowercase				: Dict     =   bos_token_id
			_lowercase				: Any     =   initializer_range






	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,self.vocab_size   )				,3				,self.vocab_size   )
			_lowercase				: Optional[int]     =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,dtype=np.intaa   ))				,-1   )

			_lowercase				: List[str]     =   shift_tokens_right(UpperCAmelCase_				,1				,2   )

			_lowercase				: Union[str, Any]     =   MarianConfig(
			    vocab_size=self.vocab_size				,d_model=self.hidden_size				,encoder_layers=self.num_hidden_layers				,decoder_layers=self.num_hidden_layers				,encoder_attention_heads=self.num_attention_heads				,decoder_attention_heads=self.num_attention_heads				,encoder_ffn_dim=self.intermediate_size				,decoder_ffn_dim=self.intermediate_size				,dropout=self.hidden_dropout_prob				,attention_dropout=self.attention_probs_dropout_prob				,max_position_embeddings=self.max_position_embeddings				,eos_token_id=self.eos_token_id				,bos_token_id=self.bos_token_id				,pad_token_id=self.pad_token_id				,initializer_range=self.initializer_range				,use_cache=UpperCAmelCase_				,)
			_lowercase				: Union[str, Any]     =   prepare_marian_inputs_dict(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   )
			return config, inputs_dict






	def    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: List[Any]     =   self.prepare_config_and_inputs()
			return config, inputs_dict






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
			_lowercase				: Dict     =   20
			_lowercase				: Dict     =   model_class_name(UpperCAmelCase_   )

			_lowercase				: str     =   model.encode(inputs_dict["""input_ids"""]   )

			_lowercase     ,  _lowercase				: int     =   (
			    inputs_dict["""decoder_input_ids"""],
			    inputs_dict["""decoder_attention_mask"""],
			)

			_lowercase				: List[Any]     =   model.init_cache(decoder_input_ids.shape[0]				,UpperCAmelCase_				,UpperCAmelCase_   )
			_lowercase				: Tuple     =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,dtype="""i4"""   )

			_lowercase				: Union[str, Any]     =   jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]				,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,)
			_lowercase				: str     =   model.decode(
			    decoder_input_ids[:, :-1]				,UpperCAmelCase_				,decoder_attention_mask=UpperCAmelCase_				,past_key_values=UpperCAmelCase_				,decoder_position_ids=UpperCAmelCase_				,)

			_lowercase				: Tuple     =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,dtype="""i4"""   )
			_lowercase				: Tuple     =   model.decode(
			    decoder_input_ids[:, -1:]				,UpperCAmelCase_				,decoder_attention_mask=UpperCAmelCase_				,past_key_values=outputs_cache.past_key_values				,decoder_position_ids=UpperCAmelCase_				,)

			_lowercase				: Optional[int]     =   model.decode(UpperCAmelCase_				,UpperCAmelCase_   )

			_lowercase				: Tuple     =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
			self.parent.assertTrue(diff < 1E-3				,msg=f"""Max diff is {diff}"""   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
			_lowercase				: int     =   20
			_lowercase				: Tuple     =   model_class_name(UpperCAmelCase_   )

			_lowercase				: Tuple     =   model.encode(inputs_dict["""input_ids"""]   )

			_lowercase     ,  _lowercase				: Union[str, Any]     =   (
			    inputs_dict["""decoder_input_ids"""],
			    inputs_dict["""decoder_attention_mask"""],
			)

			_lowercase				: Tuple     =   jnp.concatenate(
			    [
			        decoder_attention_mask,
			        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])   ),
			    ]				,axis=-1				,)

			_lowercase				: List[Any]     =   model.init_cache(decoder_input_ids.shape[0]				,UpperCAmelCase_				,UpperCAmelCase_   )
			_lowercase				: Tuple     =   jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]				,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,)

			_lowercase				: Dict     =   model.decode(
			    decoder_input_ids[:, :-1]				,UpperCAmelCase_				,decoder_attention_mask=UpperCAmelCase_				,past_key_values=UpperCAmelCase_				,decoder_position_ids=UpperCAmelCase_				,)
			_lowercase				: List[Any]     =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,dtype="""i4"""   )
			_lowercase				: List[Any]     =   model.decode(
			    decoder_input_ids[:, -1:]				,UpperCAmelCase_				,past_key_values=outputs_cache.past_key_values				,decoder_attention_mask=UpperCAmelCase_				,decoder_position_ids=UpperCAmelCase_				,)

			_lowercase				: Tuple     =   model.decode(UpperCAmelCase_				,UpperCAmelCase_				,decoder_attention_mask=UpperCAmelCase_   )

			_lowercase				: Optional[Any]     =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
			self.parent.assertTrue(diff < 1E-3				,msg=f"""Max diff is {diff}"""   )







@require_flax
class   UpperCamelCase	(     snake_case					,   unittest.TestCase					,   snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  True
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
	SCREAMING_SNAKE_CASE_							:       str						       =  (FlaxMarianMTModel,) if is_flax_available() else ()






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Any     =   FlaxMarianModelTester(self   )






	def    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: Optional[Any]     =   self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: str     =   self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward_with_attn_mask(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: int     =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__   ):
							_lowercase				: Optional[int]     =   self._prepare_for_class(UpperCAmelCase_				,UpperCAmelCase_   )
							_lowercase				: Optional[int]     =   model_class(UpperCAmelCase_   )

							@jax.jit
							def encode_jitted(UpperCAmelCase_				,UpperCAmelCase_=None				,**UpperCAmelCase_   ):
									return model.encode(input_ids=UpperCAmelCase_				,attention_mask=UpperCAmelCase_   )

							with self.subTest("""JIT Enabled"""   ):
									_lowercase				: List[Any]     =   encode_jitted(**UpperCAmelCase_   ).to_tuple()

							with self.subTest("""JIT Disabled"""   ):
									with jax.disable_jit():
											_lowercase				: Union[str, Any]     =   encode_jitted(**UpperCAmelCase_   ).to_tuple()

							self.assertEqual(len(UpperCAmelCase_   )				,len(UpperCAmelCase_   )   )
							for jitted_output, output in zip(UpperCAmelCase_				,UpperCAmelCase_   ):
									self.assertEqual(jitted_output.shape				,output.shape   )






	def    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: int     =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__   ):
							_lowercase				: Any     =   model_class(UpperCAmelCase_   )
							_lowercase				: List[str]     =   model.encode(inputs_dict["""input_ids"""]				,inputs_dict["""attention_mask"""]   )

							_lowercase				: str     =   {
							    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
							    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
							    """encoder_outputs""": encoder_outputs,
							}

							@jax.jit
							def decode_jitted(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
									return model.decode(
									    decoder_input_ids=UpperCAmelCase_				,decoder_attention_mask=UpperCAmelCase_				,encoder_outputs=UpperCAmelCase_				,)

							with self.subTest("""JIT Enabled"""   ):
									_lowercase				: List[Any]     =   decode_jitted(**UpperCAmelCase_   ).to_tuple()

							with self.subTest("""JIT Disabled"""   ):
									with jax.disable_jit():
											_lowercase				: Optional[Any]     =   decode_jitted(**UpperCAmelCase_   ).to_tuple()

							self.assertEqual(len(UpperCAmelCase_   )				,len(UpperCAmelCase_   )   )
							for jitted_output, output in zip(UpperCAmelCase_				,UpperCAmelCase_   ):
									self.assertEqual(jitted_output.shape				,output.shape   )






	@slow
	def    lowerCamelCase__					(     self   ):
			for model_class_name in self.all_model_classes:
					_lowercase				: List[Any]     =   model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""   )
					# FlaxMarianForSequenceClassification expects eos token in input_ids
					_lowercase				: Union[str, Any]     =   np.ones((1, 1)   ) * model.config.eos_token_id
					_lowercase				: Dict     =   model(UpperCAmelCase_   )
					self.assertIsNotNone(UpperCAmelCase_   )







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     unittest.TestCase       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  None
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  None






	@classmethod
	def    lowerCamelCase__					(     cls   ):
			_lowercase				: str     =   f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
			return cls






	@cached_property
	def    lowerCamelCase__					(     self   ):
			return MarianTokenizer.from_pretrained(self.model_name   )






	@property
	def    lowerCamelCase__					(     self   ):
			return self.tokenizer.eos_token_id






	@cached_property
	def    lowerCamelCase__					(     self   ):
			_lowercase				: FlaxMarianMTModel     =   FlaxMarianMTModel.from_pretrained(self.model_name   )
			self.assertEqual(model.config.decoder_start_token_id				,model.config.pad_token_id   )
			return model






	def    lowerCamelCase__					(     self				,**UpperCAmelCase_   ):
			_lowercase				: int     =   self.translate_src_text(**UpperCAmelCase_   )
			self.assertListEqual(self.expected_text				,UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,**UpperCAmelCase_   ):
			_lowercase				: Dict     =   self.tokenizer(self.src_text				,padding=UpperCAmelCase_				,return_tensors="""np"""				,**UpperCAmelCase_   )
			_lowercase				: List[Any]     =   self.model.generate(
			    model_inputs.input_ids				,attention_mask=model_inputs.attention_mask				,num_beams=2				,max_length=1_28				,).sequences
			_lowercase				: int     =   self.tokenizer.batch_decode(UpperCAmelCase_				,skip_special_tokens=UpperCAmelCase_   )
			return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  "en"
	SCREAMING_SNAKE_CASE_							:       Any						       =  "fr"
	SCREAMING_SNAKE_CASE_							:       Dict						       =  [
	    "I am a small frog.",
	    "Now I can forget the 100 words of german that I know.",
	]
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  [
	    "Je suis une petite grenouille.",
	    "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
	]






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       str						       =  "fr"
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  "en"
	SCREAMING_SNAKE_CASE_							:       Any						       =  [
	    "Donnez moi le micro.",
	    "Tom et Mary étaient assis à une table.",  # Accents
	]
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  [
	    "Give me the microphone.",
	    "Tom and Mary were sitting at a table.",
	]






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  "mt"
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  "en"
	SCREAMING_SNAKE_CASE_							:       List[Any]						       =  ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
	SCREAMING_SNAKE_CASE_							:       Any						       =  ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Any						       =  "en"
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  "de"
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  [
	    "I am a small frog.",
	    "Now I can forget the 100 words of german that I know.",
	    "Tom asked his teacher for advice.",
	    "That's how I would do it.",
	    "Tom really admired Mary's courage.",
	    "Turn around and close your eyes.",
	]
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  [
	    "Ich bin ein kleiner Frosch.",
	    "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
	    "Tom bat seinen Lehrer um Rat.",
	    "So würde ich das machen.",
	    "Tom bewunderte Marias Mut wirklich.",
	    "Drehen Sie sich um und schließen Sie die Augen.",
	]






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  "en"
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  "zh"
	SCREAMING_SNAKE_CASE_							:       Any						       =  ["My name is Wolfgang and I live in Berlin"]
	SCREAMING_SNAKE_CASE_							:       Any						       =  ["我叫沃尔夫冈 我住在柏林"]






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Dict						       =  "ru"
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  "fr"
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  ["Он показал мне рукопись своей новой пьесы."]
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  ["Il m'a montré le manuscrit de sa nouvelle pièce."]






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Dict						       =  "en"
	SCREAMING_SNAKE_CASE_							:       List[str]						       =  "ROMANCE"
	SCREAMING_SNAKE_CASE_							:       List[Any]						       =  [
	    ">>fr<< Don't spend so much time watching TV.",
	    ">>pt<< Your message has been sent.",
	    ">>es<< He's two years older than me.",
	]
	SCREAMING_SNAKE_CASE_							:       int						       =  [
	    "Ne passez pas autant de temps à regarder la télé.",
	    "A sua mensagem foi enviada.",
	    "Es dos años más viejo que yo.",
	]






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()



