






"""simple docstring"""

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

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
			lowercase           =							'''platform'''

			import jax
			import jax.numpy as jnp

			from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def _lowerCAmelCase						(	__lowerCamelCase:Dict			,    __lowerCamelCase:List[str]			,    __lowerCamelCase:Optional[int]=None			,    __lowerCamelCase:List[str]=None ):

						'''simple docstring'''






						if attention_mask is None:
												__magic_name__      =  np.where(input_ids != config.pad_token_id			,    1			,    0 )
						return {
						    "input_ids": input_ids,
						    "attention_mask": attention_mask,
						}

@require_flax
class     A_				:







				def __init__(				self				:    Tuple    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    int=1_3    ,     __lowerCamelCase				:    Union[str, Any]=7    ,     __lowerCamelCase				:    Union[str, Any]=True    ,     __lowerCamelCase				:    Dict=False    ,     __lowerCamelCase				:    Dict=9_9    ,     __lowerCamelCase				:    List[str]=1_6    ,     __lowerCamelCase				:    Optional[int]=2    ,     __lowerCamelCase				:    Dict=4    ,     __lowerCamelCase				:    Tuple=4    ,     __lowerCamelCase				:    str="gelu"    ,     __lowerCamelCase				:    Optional[Any]=0.1    ,     __lowerCamelCase				:    Optional[Any]=0.1    ,     __lowerCamelCase				:    int=2_0    ,     __lowerCamelCase				:    Dict=2    ,     __lowerCamelCase				:    Optional[Any]=1    ,     __lowerCamelCase				:    Optional[Any]=0    ,     __lowerCamelCase				:    List[str]=1_6    ,     __lowerCamelCase				:    Tuple=1_6    ,     __lowerCamelCase				:    int=0.02    ,     )					->       List[Any]:
										__magic_name__      =  parent
										__magic_name__      =  batch_size
										__magic_name__      =  seq_length
										__magic_name__      =  is_training
										__magic_name__      =  use_labels
										__magic_name__      =  vocab_size
										__magic_name__      =  hidden_size
										__magic_name__      =  num_hidden_layers
										__magic_name__      =  num_attention_heads
										__magic_name__      =  intermediate_size
										__magic_name__      =  hidden_act
										__magic_name__      =  hidden_dropout_prob
										__magic_name__      =  attention_probs_dropout_prob
										__magic_name__      =  max_position_embeddings
										__magic_name__      =  eos_token_id
										__magic_name__      =  pad_token_id
										__magic_name__      =  bos_token_id
										__magic_name__      =  embed_dim
										__magic_name__      =  word_embed_proj_dim
										__magic_name__      =  initializer_range
										__magic_name__      =  False







				def      _snake_case       (				self				:    Any						)					->       Tuple:
										__magic_name__      =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,     self.vocab_size						)    ,     3    ,     self.vocab_size						)
										__magic_name__      =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,     dtype=np.intaa						))    ,     -1						)

										__magic_name__      =  OPTConfig(
										    vocab_size=self.vocab_size    ,     hidden_size=self.hidden_size    ,     num_hidden_layers=self.num_hidden_layers    ,     num_attention_heads=self.num_attention_heads    ,     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    ,     embed_dim=self.embed_dim    ,     is_encoder_decoder=lowerCAmelCase__    ,     word_embed_proj_dim=self.word_embed_proj_dim    ,     initializer_range=self.initializer_range    ,     use_cache=lowerCAmelCase__    ,     )
										__magic_name__      =  prepare_opt_inputs_dict(lowerCAmelCase__    ,     lowerCAmelCase__						)
										return config, inputs_dict







				def      _snake_case       (				self				:    Dict						)					->       List[Any]:
										__magic_name__   ,     __magic_name__      =  self.prepare_config_and_inputs()
										return config, inputs_dict







				def      _snake_case       (				self				:    Any    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    Optional[int]						)					->       Tuple:
										__magic_name__      =  2_0
										__magic_name__      =  model_class_name(lowerCAmelCase__						)

										__magic_name__      =  inputs_dict["input_ids"]
										__magic_name__      =  inputs_dict["attention_mask"]

										__magic_name__      =  model.init_cache(input_ids.shape[0]    ,     lowerCAmelCase__						)
										__magic_name__      =  jnp.ones((input_ids.shape[0], max_length)    ,     dtype="i4"						)

										__magic_name__      =  jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1						)[None, :]    ,     (input_ids.shape[0], input_ids.shape[-1] - 1)    ,     )
										__magic_name__      =  model(
										    input_ids[:, :-1]    ,     attention_mask=lowerCAmelCase__    ,     past_key_values=lowerCAmelCase__    ,     position_ids=lowerCAmelCase__    ,     )

										__magic_name__      =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,     dtype="i4"						)
										__magic_name__      =  model(
										    input_ids[:, -1:]    ,     attention_mask=lowerCAmelCase__    ,     past_key_values=outputs_cache.past_key_values    ,     position_ids=lowerCAmelCase__    ,     )

										__magic_name__      =  model(lowerCAmelCase__						)

										__magic_name__      =  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      _snake_case       (				self				:    int    ,     __lowerCamelCase				:    Optional[Any]    ,     __lowerCamelCase				:    List[str]    ,     __lowerCamelCase				:    int						)					->       Dict:
										__magic_name__      =  2_0
										__magic_name__      =  model_class_name(lowerCAmelCase__						)

										__magic_name__   ,     __magic_name__      =  (
										    inputs_dict["input_ids"],
										    inputs_dict["attention_mask"],
										)

										__magic_name__      =  jnp.concatenate(
										    [
										        attention_mask,
										        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])						),
										    ]    ,     axis=-1    ,     )

										__magic_name__      =  model.init_cache(input_ids.shape[0]    ,     lowerCAmelCase__						)
										__magic_name__      =  jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1						)[None, :]    ,     (input_ids.shape[0], input_ids.shape[-1] - 1)    ,     )

										__magic_name__      =  model(
										    input_ids[:, :-1]    ,     attention_mask=lowerCAmelCase__    ,     past_key_values=lowerCAmelCase__    ,     position_ids=lowerCAmelCase__    ,     )
										__magic_name__      =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,     dtype="i4"						)
										__magic_name__      =  model(
										    input_ids[:, -1:]    ,     past_key_values=outputs_cache.past_key_values    ,     attention_mask=lowerCAmelCase__    ,     position_ids=lowerCAmelCase__    ,     )

										__magic_name__      =  model(lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__						)

										__magic_name__      =  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     A_				(   _snake_case       ,				unittest.TestCase       ,				_snake_case	):
				UpperCAmelCase__										=					(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
				UpperCAmelCase__										=					() if is_flax_available() else ()







				def      _snake_case       (				self				:    Optional[Any]						)					->       str:
										__magic_name__      =  FlaxOPTModelTester(self						)







				def      _snake_case       (				self				:    List[Any]						)					->       int:
										__magic_name__   ,     __magic_name__      =  self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__						)







				def      _snake_case       (				self				:    Optional[int]						)					->       List[Any]:
										__magic_name__   ,     __magic_name__      =  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(lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__						)







				@slow
				def      _snake_case       (				self				:    Union[str, Any]						)					->       Optional[Any]:
										for model_class_name in self.all_model_classes:
																__magic_name__      =  model_class_name.from_pretrained("facebook/opt-125m"						)
																__magic_name__      =  np.ones((1, 1)						) * model.config.eos_token_id
																__magic_name__      =  model(lowerCAmelCase__						)
																self.assertIsNotNone(lowerCAmelCase__						)





@require_sentencepiece
@require_flax
class     A_				(   unittest.TestCase	):







				@slow
				def      _snake_case       (				self				:    Dict						)					->       Optional[Any]:
										__magic_name__      =  FlaxOPTModel.from_pretrained("facebook/opt-350m"						)
										__magic_name__      =  jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]						)
										__magic_name__      =  model(input_ids=lowerCAmelCase__						).last_hidden_state
										__magic_name__      =  (1, 1_1, 5_1_2)
										self.assertEqual(output.shape    ,     lowerCAmelCase__						)
										__magic_name__      =  jnp.array(
										    [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]						)
										self.assertTrue(jnp.allclose(output[:, :3, :3]    ,     lowerCAmelCase__    ,     atol=4e-2						)						)





@require_flax
@slow
class     A_				(   unittest.TestCase	):







				def      _snake_case       (				self				:    Tuple						)					->       List[Any]:
										super().setUp()
										__magic_name__      =  "facebook/opt-350m"







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Union[str, Any]:
										__magic_name__      =  FlaxOPTForCausalLM.from_pretrained(self.path_model						)
										__magic_name__      =  GPTaTokenizer.from_pretrained(self.path_model						)

										__magic_name__      =  [
										    "Today is a beautiful day and I want to",
										    "In the city of",
										    "Paris is the capital of France and",
										    "Computers and mobile phones have taken",
										]
										# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
										__magic_name__      =  tokenizer(lowerCAmelCase__    ,     return_tensors="jax"    ,     padding=lowerCAmelCase__    ,     add_special_tokens=lowerCAmelCase__						)
										__magic_name__      =  model(inputs.input_ids    ,     attention_mask=inputs.attention_mask						)[0].mean(axis=-1						)
										__magic_name__      =  jnp.array(
										    [
										        [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
										        [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
										        [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
										        [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
										    ]						)
										self.assertTrue(jnp.allclose(lowerCAmelCase__    ,     lowerCAmelCase__    ,     atol=4e-2						)						)

										__magic_name__      =  jax.jit(lowerCAmelCase__						)
										__magic_name__      =  model(inputs.input_ids    ,     attention_mask=inputs.attention_mask						)[0].mean(axis=-1						)
										self.assertTrue(jnp.allclose(lowerCAmelCase__    ,     lowerCAmelCase__    ,     atol=4e-2						)						)





@require_flax
@slow
class     A_				(   unittest.TestCase	):







				@property
				def      _snake_case       (				self				:    Optional[Any]						)					->       Optional[Any]:
										return [
										    "Today is a beautiful day and I want",
										    "In the city of",
										    "Paris is the capital of France and",
										    "Computers and mobile phones have taken",
										]







				def      _snake_case       (				self				:    Dict						)					->       Dict:
										__magic_name__      =  "facebook/opt-125m"

										__magic_name__      =  [
										    "Today is a beautiful day and I want to",
										    "In the city of New York, the city",
										    "Paris is the capital of France and the capital",
										    "Computers and mobile phones have taken over the",
										]

										__magic_name__      =  []

										__magic_name__      =  FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__						)
										__magic_name__      =  GPTaTokenizer.from_pretrained(lowerCAmelCase__						)

										for prompt in self.prompts:
																__magic_name__      =  tokenizer(lowerCAmelCase__    ,     return_tensors="jax"						).input_ids

																__magic_name__      =  model.generate(lowerCAmelCase__    ,     max_length=1_0						)
																__magic_name__      =  generated_ids[0]

																__magic_name__      =  tokenizer.batch_decode(lowerCAmelCase__    ,     skip_special_tokens=lowerCAmelCase__						)
																predicted_outputs += generated_string

										self.assertListEqual(lowerCAmelCase__    ,     lowerCAmelCase__						)







				def      _snake_case       (				self				:    Any						)					->       Optional[Any]:
										__magic_name__      =  "facebook/opt-350m"

										__magic_name__      =  [
										    "Today is a beautiful day and I want to",
										    "In the city of San Francisco, the city",
										    "Paris is the capital of France and the capital",
										    "Computers and mobile phones have taken over the",
										]

										__magic_name__      =  []
										__magic_name__      =  FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__						)
										__magic_name__      =  GPTaTokenizer.from_pretrained(lowerCAmelCase__						)

										for prompt in self.prompts:
																__magic_name__      =  tokenizer(lowerCAmelCase__    ,     return_tensors="jax"						).input_ids

																__magic_name__      =  model.generate(lowerCAmelCase__    ,     max_length=1_0						)
																__magic_name__      =  generated_ids[0]

																__magic_name__      =  tokenizer.batch_decode(lowerCAmelCase__    ,     skip_special_tokens=lowerCAmelCase__						)
																predicted_outputs += generated_string

										self.assertListEqual(lowerCAmelCase__    ,     lowerCAmelCase__						)







				def      _snake_case       (				self				:    str						)					->       Any:
										__magic_name__      =  "facebook/opt-125m"
										__magic_name__      =  [
										    "Today is a beautiful day and I want to thank",
										    "In the city of Rome Canaver Canaver Canaver Canaver",
										]
										__magic_name__      =  FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__						)
										__magic_name__      =  GPTaTokenizer.from_pretrained(lowerCAmelCase__						)
										__magic_name__      =  tokenizer(
										    [
										        "Today is a beautiful day and I want to",
										        "In the city of",
										    ]    ,     return_tensors="jax"    ,     padding=lowerCAmelCase__    ,     )

										__magic_name__      =  jax.jit(model.generate						)

										__magic_name__      =  jit_generate(inputs["input_ids"]    ,     attention_mask=inputs["attention_mask"]						).sequences

										__magic_name__      =  tokenizer.batch_decode(lowerCAmelCase__    ,     skip_special_tokens=lowerCAmelCase__						)

										self.assertIsNotNone(lowerCAmelCase__    ,     lowerCAmelCase__						)







				def      _snake_case       (				self				:    str						)					->       Any:
										__magic_name__      =  "facebook/opt-350m"

										__magic_name__      =  GPTaTokenizer.from_pretrained(lowerCAmelCase__						)
										__magic_name__      =  FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__						)

										__magic_name__      =  "left"

										# use different length sentences to test batching
										__magic_name__      =  [
										    "Hello, my dog is a little",
										    "Today, I",
										]

										__magic_name__      =  tokenizer(lowerCAmelCase__    ,     return_tensors="jax"    ,     padding=lowerCAmelCase__						)
										__magic_name__      =  inputs["input_ids"]

										__magic_name__      =  model.generate(input_ids=lowerCAmelCase__    ,     attention_mask=inputs["attention_mask"]    ,     trace=lowerCAmelCase__						)

										__magic_name__      =  tokenizer(sentences[0]    ,     return_tensors="jax"						).input_ids
										__magic_name__      =  model.generate(input_ids=lowerCAmelCase__						)

										__magic_name__      =  inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
										__magic_name__      =  tokenizer(sentences[1]    ,     return_tensors="jax"						).input_ids
										__magic_name__      =  model.generate(input_ids=lowerCAmelCase__    ,     max_length=model.config.max_length - num_paddings						)

										__magic_name__      =  tokenizer.batch_decode(outputs[0]    ,     skip_special_tokens=lowerCAmelCase__						)
										__magic_name__      =  tokenizer.decode(output_non_padded[0][0]    ,     skip_special_tokens=lowerCAmelCase__						)
										__magic_name__      =  tokenizer.decode(output_padded[0][0]    ,     skip_special_tokens=lowerCAmelCase__						)

										__magic_name__      =  [
										    "Hello, my dog is a little bit of a dork.\nI'm a little bit",
										    "Today, I was in the middle of a conversation with a friend about the",
										]
										self.assertListEqual(lowerCAmelCase__    ,     lowerCAmelCase__						)
										self.assertListEqual(lowerCAmelCase__    ,     [non_padded_sentence, padded_sentence]						)






import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter






class     __magic_name__     (							TensorFormatter[Mapping, np.ndarray, Mapping]     ):


 def __init__(    self		:			Optional[int]      ,    lowerCAmelCase__		:			str=None      ,    **lowerCAmelCase__		:			int     )     ->   Optional[Any]:
   super().__init__(features=lowerCAmelCase__     )
   UpperCAmelCase          =	np_array_kwargs


 def _UpperCamelCase						(    self		:			Any      ,    lowerCAmelCase__		:			Dict     )     ->   Union[str, Any]:
   if isinstance(lowerCAmelCase__      ,    lowerCAmelCase__     ):
     if column and all(
         isinstance(lowerCAmelCase__      ,    np.ndarray     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
       return np.stack(lowerCAmelCase__     )
     else:
       # don't use np.array(column, dtype=object)
       # since it fails in certain cases
       # see https://stackoverflow.com/q/51005699
       UpperCAmelCase          =	np.empty(len(lowerCAmelCase__     )      ,    dtype=lowerCAmelCase__     )
       UpperCAmelCase          =	column
       return out
   return column


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			Union[str, Any]     )     ->   List[Any]:
   if isinstance(lowerCAmelCase__      ,    (str, bytes, type(lowerCAmelCase__     ))     ):
     return value
   elif isinstance(lowerCAmelCase__      ,    (np.character, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.character     ):
     return value
   elif isinstance(lowerCAmelCase__      ,    np.number     ):
     return value

   UpperCAmelCase          =	{}

   if isinstance(lowerCAmelCase__      ,    np.ndarray     ) and np.issubdtype(value.dtype      ,    np.integer     ):
     UpperCAmelCase          =	{"dtype": np.intaa}
   elif isinstance(lowerCAmelCase__      ,    np.ndarray     ) and np.issubdtype(value.dtype      ,    np.floating     ):
     UpperCAmelCase          =	{"dtype": np.floataa}
   elif config.PIL_AVAILABLE and "PIL" in sys.modules:
     import PIL.Image

     if isinstance(lowerCAmelCase__      ,    PIL.Image.Image     ):
       return np.asarray(lowerCAmelCase__      ,    **self.np_array_kwargs     )

   return np.asarray(lowerCAmelCase__      ,    **{**default_dtype, **self.np_array_kwargs}     )


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			str     )     ->   Optional[int]:
   # support for torch, tf, jax etc.
   if config.TORCH_AVAILABLE and "torch" in sys.modules:
     import torch

     if isinstance(lowerCAmelCase__      ,    torch.Tensor     ):
       return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
   if hasattr(lowerCAmelCase__      ,    "__array__"     ) and not isinstance(lowerCAmelCase__      ,    (np.ndarray, np.character, np.number)     ):
     UpperCAmelCase          =	data_struct.__array__()
   # support for nested types like struct of list of struct
   if isinstance(lowerCAmelCase__      ,    np.ndarray     ):
     if data_struct.dtype == object:
       return self._consolidate([self.recursive_tensorize(lowerCAmelCase__     ) for substruct in data_struct]     )
   if isinstance(lowerCAmelCase__      ,    (list, tuple)     ):
     return self._consolidate([self.recursive_tensorize(lowerCAmelCase__     ) for substruct in data_struct]     )
   return self._tensorize(lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			dict     )     ->   Union[str, Any]:
   return map_nested(self._recursive_tensorize      ,    lowerCAmelCase__      ,    map_list=lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			int      ,    lowerCAmelCase__		:			pa.Table     )     ->   Mapping:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_row(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_row(lowerCAmelCase__     )
   return self.recursive_tensorize(lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			pa.Table     )     ->   np.ndarray:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_column(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_column(lowerCAmelCase__      ,    pa_table.column_names[0]     )
   UpperCAmelCase          =	self.recursive_tensorize(lowerCAmelCase__     )
   UpperCAmelCase          =	self._consolidate(lowerCAmelCase__     )
   return column


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			pa.Table     )     ->   Mapping:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_batch(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_batch(lowerCAmelCase__     )
   UpperCAmelCase          =	self.recursive_tensorize(lowerCAmelCase__     )
   for column_name in batch:
     UpperCAmelCase          =	self._consolidate(batch[column_name]     )
   return batch



