




'''simple docstring'''






import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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


if is_flax_available():
      import jax
      import jax.numpy as jnp

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
      import torch

class 	SCREAMING_SNAKE_CASE_		:



 def __init__(	self ,		A ,		A=14 ,		A=7 ,		A=True ,		A=True ,		A=False ,		A=True ,		A=99 ,		A=32 ,		A=4 ,		A=4 ,		A=[[["global", "local"], 2]] ,		A=37 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=512 ,		A=7 ,		A=0.0_2 ,		):






        '''simple docstring'''




        lowercase	:		Dict    =   parent
        lowercase	:		int    =   batch_size
        lowercase	:		List[str]    =   seq_length
        lowercase	:		List[str]    =   is_training
        lowercase	:		Optional[int]    =   use_input_mask
        lowercase	:		List[str]    =   use_token_type_ids
        lowercase	:		Tuple    =   use_labels
        lowercase	:		List[Any]    =   vocab_size
        lowercase	:		int    =   hidden_size
        lowercase	:		Union[str, Any]    =   num_hidden_layers
        lowercase	:		Dict    =   num_attention_heads
        lowercase	:		str    =   attention_types
        lowercase	:		Any    =   intermediate_size
        lowercase	:		Tuple    =   hidden_act
        lowercase	:		Union[str, Any]    =   hidden_dropout_prob
        lowercase	:		str    =   attention_probs_dropout_prob
        lowercase	:		str    =   max_position_embeddings
        lowercase	:		str    =   window_size
        lowercase	:		Any    =   initializer_range
        lowercase	:		Optional[int]    =   None
        lowercase	:		Union[str, Any]    =   vocab_size - 1
        lowercase	:		Union[str, Any]    =   vocab_size - 1
        lowercase	:		str    =   vocab_size - 1



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		List[str]    =   ids_tensor([self.batch_size, self.seq_length] ,		self.vocab_size			)

        lowercase	:		Optional[int]    =   None
        if self.use_input_mask:
               lowercase	:		str    =   random_attention_mask([self.batch_size, self.seq_length]			)

        lowercase	:		Dict    =   GPTNeoConfig(
            vocab_size=self.vocab_size ,		hidden_size=self.hidden_size ,		num_layers=self.num_hidden_layers ,		num_heads=self.num_attention_heads ,		max_position_embeddings=self.max_position_embeddings ,		use_cache=__lowerCamelCase ,		bos_token_id=self.bos_token_id ,		eos_token_id=self.eos_token_id ,		pad_token_id=self.pad_token_id ,		window_size=self.window_size ,		attention_types=self.attention_types ,		)

        return (config, input_ids, input_mask)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Optional[Any]    =   self.prepare_config_and_inputs()
        lowercase	:		Any    =   config_and_inputs
        lowercase	:		List[str]    =   {"""input_ids""": input_ids, """attention_mask""": attention_mask}
        return config, inputs_dict



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Optional[int]    =   20
        lowercase	:		Optional[Any]    =   model_class_name(__lowerCamelCase			)

        lowercase	:		List[str]    =   model.init_cache(input_ids.shape[0] ,		__lowerCamelCase			)
        lowercase	:		Dict    =   jnp.ones((input_ids.shape[0], max_decoder_length) ,		dtype='''i4'''			)

        lowercase	:		List[str]    =   jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1			)[None, :] ,		(input_ids.shape[0], input_ids.shape[-1] - 1)			)
        lowercase	:		int    =   model(
            input_ids[:, :-1] ,		attention_mask=__lowerCamelCase ,		past_key_values=__lowerCamelCase ,		position_ids=__lowerCamelCase ,		)

        lowercase	:		List[str]    =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,		dtype='''i4'''			)
        lowercase	:		List[str]    =   model(
            input_ids[:, -1:] ,		attention_mask=__lowerCamelCase ,		past_key_values=outputs_cache.past_key_values ,		position_ids=__lowerCamelCase ,		)

        lowercase	:		Dict    =   model(__lowerCamelCase			)

        lowercase	:		List[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}"""			)





 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Dict    =   20
        lowercase	:		str    =   model_class_name(__lowerCamelCase			)

        lowercase	:		int    =   jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])			)] ,		axis=-1 ,		)

        lowercase	:		Optional[Any]    =   model.init_cache(input_ids.shape[0] ,		__lowerCamelCase			)
        lowercase	:		List[Any]    =   jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1			)[None, :] ,		(input_ids.shape[0], input_ids.shape[-1] - 1)			)

        lowercase	:		Dict    =   model(
            input_ids[:, :-1] ,		attention_mask=__lowerCamelCase ,		past_key_values=__lowerCamelCase ,		position_ids=__lowerCamelCase ,		)
        lowercase	:		Optional[int]    =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,		dtype='''i4'''			)
        lowercase	:		int    =   model(
            input_ids[:, -1:] ,		past_key_values=outputs_cache.past_key_values ,		attention_mask=__lowerCamelCase ,		position_ids=__lowerCamelCase ,		)

        lowercase	:		Optional[Any]    =   model(__lowerCamelCase ,		attention_mask=__lowerCamelCase			)

        lowercase	:		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 	SCREAMING_SNAKE_CASE_		(							__snake_case		,  __snake_case		,  unittest.TestCase					):
 __snake_case						:  Optional[Any]       =   (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
 __snake_case						:  Union[str, Any]       =   (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Dict    =   FlaxGPTNeoModelTester(self			)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        for model_class_name in self.all_model_classes:
               lowercase	:		str    =   self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase			)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        for model_class_name in self.all_model_classes:
               lowercase	:		List[Any]    =   self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward_with_attn_mask(
                   __lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase			)



 @slow
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		List[str]    =   GPTaTokenizer.from_pretrained('''gpt2''' ,		pad_token='''<|endoftext|>''' ,		padding_side='''left'''			)
        lowercase	:		int    =   tokenizer(['''Hello this is a long string''', '''Hey'''] ,		return_tensors='''np''' ,		padding=__lowerCamelCase ,		truncation=__lowerCamelCase			)

        lowercase	:		str    =   FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M'''			)
        lowercase	:		int    =   False
        lowercase	:		List[Any]    =   model.config.eos_token_id

        lowercase	:		Any    =   jax.jit(model.generate			)

        lowercase	:		Union[str, Any]    =   jit_generate(
            inputs['''input_ids'''] ,		attention_mask=inputs['''attention_mask'''] ,		pad_token_id=tokenizer.pad_token_id			).sequences

        lowercase	:		int    =   tokenizer.batch_decode(__lowerCamelCase ,		skip_special_tokens=__lowerCamelCase			)

        lowercase	:		int    =   [
            """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
            """Hey, I'm a little late to the party. I'm going to""",
        ]

        self.assertListEqual(__lowerCamelCase ,		__lowerCamelCase			)



 @is_pt_flax_cross_test
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Tuple    =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__			):
                      # prepare inputs
                      lowercase	:		Union[str, Any]    =   self._prepare_for_class(__lowerCamelCase ,		__lowerCamelCase			)
                      lowercase	:		Optional[Any]    =   {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

                      # load corresponding PyTorch class
                      lowercase	:		Dict    =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      lowercase	:		Optional[Any]    =   getattr(__lowerCamelCase ,		__lowerCamelCase			)

                      lowercase	:		Union[str, Any]    =   pt_inputs["""input_ids"""].shape
                      lowercase	:		Union[str, Any]    =   np.random.randint(0 ,		seq_length - 1 ,		size=(batch_size,)			)
                      for batch_idx, start_index in enumerate(__lowerCamelCase			):
                             lowercase	:		List[str]    =   0
                             lowercase	:		str    =   1
                             lowercase	:		Tuple    =   0
                             lowercase	:		List[Any]    =   1
                      lowercase	:		List[str]    =   pt_model_class(__lowerCamelCase			).eval()
                      lowercase	:		Optional[Any]    =   model_class(__lowerCamelCase ,		dtype=jnp.floataa			)

                      lowercase	:		List[Any]    =   convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,		__lowerCamelCase			)
                      lowercase	:		Union[str, Any]    =   fx_state

                      with torch.no_grad():
                             lowercase	:		Any    =   pt_model(**__lowerCamelCase			).to_tuple()

                      lowercase	:		Dict    =   fx_model(**__lowerCamelCase			).to_tuple()
                      self.assertEqual(len(__lowerCamelCase			) ,		len(__lowerCamelCase			) ,		'''Output lengths differ between Flax and PyTorch'''			)
                      for fx_output, pt_output in zip(__lowerCamelCase ,		__lowerCamelCase			):
                             self.assert_almost_equals(fx_output[:, -1] ,		pt_output[:, -1].numpy() ,		4E-2			)

                      with tempfile.TemporaryDirectory() as tmpdirname:
                             pt_model.save_pretrained(__lowerCamelCase			)
                             lowercase	:		Tuple    =   model_class.from_pretrained(__lowerCamelCase ,		from_pt=__lowerCamelCase			)

                      lowercase	:		Union[str, Any]    =   fx_model_loaded(**__lowerCamelCase			).to_tuple()
                      self.assertEqual(
                          len(__lowerCamelCase			) ,		len(__lowerCamelCase			) ,		'''Output lengths differ between Flax and PyTorch'''			)
                      for fx_output_loaded, pt_output in zip(__lowerCamelCase ,		__lowerCamelCase			):
                             self.assert_almost_equals(fx_output_loaded[:, -1] ,		pt_output[:, -1].numpy() ,		4E-2			)



 @is_pt_flax_cross_test
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Tuple    =   self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__			):
                      # prepare inputs
                      lowercase	:		int    =   self._prepare_for_class(__lowerCamelCase ,		__lowerCamelCase			)
                      lowercase	:		Any    =   {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

                      # load corresponding PyTorch class
                      lowercase	:		List[Any]    =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      lowercase	:		Union[str, Any]    =   getattr(__lowerCamelCase ,		__lowerCamelCase			)

                      lowercase	:		Optional[Any]    =   pt_model_class(__lowerCamelCase			).eval()
                      lowercase	:		Dict    =   model_class(__lowerCamelCase ,		dtype=jnp.floataa			)

                      lowercase	:		str    =   load_flax_weights_in_pytorch_model(__lowerCamelCase ,		fx_model.params			)
                      lowercase	:		Optional[int]    =   pt_inputs["""input_ids"""].shape
                      lowercase	:		Optional[int]    =   np.random.randint(0 ,		seq_length - 1 ,		size=(batch_size,)			)
                      for batch_idx, start_index in enumerate(__lowerCamelCase			):
                             lowercase	:		Dict    =   0
                             lowercase	:		Any    =   1
                             lowercase	:		int    =   0
                             lowercase	:		str    =   1

                      # make sure weights are tied in PyTorch
                      pt_model.tie_weights()

                      with torch.no_grad():
                             lowercase	:		Optional[Any]    =   pt_model(**__lowerCamelCase			).to_tuple()

                      lowercase	:		str    =   fx_model(**__lowerCamelCase			).to_tuple()
                      self.assertEqual(len(__lowerCamelCase			) ,		len(__lowerCamelCase			) ,		'''Output lengths differ between Flax and PyTorch'''			)
                      for fx_output, pt_output in zip(__lowerCamelCase ,		__lowerCamelCase			):
                             self.assert_almost_equals(fx_output[:, -1] ,		pt_output[:, -1].numpy() ,		4E-2			)

                      with tempfile.TemporaryDirectory() as tmpdirname:
                             fx_model.save_pretrained(__lowerCamelCase			)
                             lowercase	:		Tuple    =   pt_model_class.from_pretrained(__lowerCamelCase ,		from_flax=__lowerCamelCase			)

                      with torch.no_grad():
                             lowercase	:		List[str]    =   pt_model_loaded(**__lowerCamelCase			).to_tuple()

                      self.assertEqual(
                          len(__lowerCamelCase			) ,		len(__lowerCamelCase			) ,		'''Output lengths differ between Flax and PyTorch'''			)
                      for fx_output, pt_output in zip(__lowerCamelCase ,		__lowerCamelCase			):
                             self.assert_almost_equals(fx_output[:, -1] ,		pt_output[:, -1].numpy() ,		4E-2			)





 @slow
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        for model_class_name in self.all_model_classes:
               lowercase	:		int    =   model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M'''			)
               lowercase	:		Union[str, Any]    =   model(np.ones((1, 1)			)			)
               self.assertIsNotNone(__lowerCamelCase			)





import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


__lowerCAmelCase    :  Tuple										= logging.get_logger(__name__)
class 				lowerCamelCase			(							__snake_case					):






      def __init__(      self  ,		*__lowerCamelCase  ,		**__lowerCamelCase						)     ->				None:


          '''simple docstring'''







          warnings.warn(
              """The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
              """ Please use ConditionalDetrImageProcessor instead."""  ,		__lowerCamelCase  ,		)
          super().__init__(*__lowerCamelCase  ,		**__lowerCamelCase						)


