
"""simple docstring"""


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
		import torch







class 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      str  ,A_     :      Union[str, Any]  ,A_     :      Optional[Any]=14  ,A_     :      Any=7  ,A_     :      Union[str, Any]=True  ,A_     :      Optional[int]=True  ,A_     :      Optional[Any]=False  ,A_     :      int=True  ,A_     :      Any=99  ,A_     :      Dict=32  ,A_     :      str=4  ,A_     :      Any=4  ,A_     :      Optional[Any]=4  ,A_     :      List[Any]=37  ,A_     :      Optional[int]="gelu"  ,A_     :      Dict=0.1  ,A_     :      Optional[int]=0.1  ,A_     :      Any=512  ,A_     :      int=0.02  ,)     ->   int:
							A						   =   parent
							A						   =   batch_size
							A						   =   seq_length
							A						   =   is_training
							A						   =   use_input_mask
							A						   =   use_token_type_ids
							A						   =   use_labels
							A						   =   vocab_size
							A						   =   hidden_size
							A						   =   rotary_dim
							A						   =   num_hidden_layers
							A						   =   num_attention_heads
							A						   =   intermediate_size
							A						   =   hidden_act
							A						   =   hidden_dropout_prob
							A						   =   attention_probs_dropout_prob
							A						   =   max_position_embeddings
							A						   =   initializer_range
							A						   =   None
							A						   =   vocab_size - 1
							A						   =   vocab_size - 1
							A						   =   vocab_size - 1


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Optional[int]:
							A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size      )

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

							A						   =   GPTJConfig(
							    vocab_size=self.vocab_size  ,n_embd=self.hidden_size  ,n_layer=self.num_hidden_layers  ,n_head=self.num_attention_heads  ,n_positions=self.max_position_embeddings  ,use_cache=A_  ,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 (config, input_ids, input_mask)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Union[str, Any]:
							A						   =   self.prepare_config_and_inputs()
							A   ,      A   ,      A						   =   config_and_inputs
							A						   =   {'input_ids': input_ids, 'attention_mask': attention_mask}
							return config, inputs_dict


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Any  ,A_     :      List[str]  ,A_     :      List[str]  ,A_     :      Tuple      )     ->   int:
							A						   =   20
							A						   =   model_class_name(A_      )

							A						   =   model.init_cache(input_ids.shape[0]  ,A_      )
							A						   =   jnp.ones((input_ids.shape[0], max_decoder_length)  ,dtype='i4'      )

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

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

							A						   =   model(A_      )

							A						   =   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     :      str  ,A_     :      Optional[int]  ,A_     :      Optional[int]  ,A_     :      Tuple  ,A_     :      Union[str, Any]      )     ->   Optional[Any]:
							A						   =   20
							A						   =   model_class_name(A_      )

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

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

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

							A						   =   model(A_  ,attention_mask=A_      )

							A						   =   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 				lowerCAmelCase_				(      _lowercase		,					_lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
						_lowerCamelCase:    Optional[Any]      =    (FlaxGPTJForCausalLM,) if is_flax_available() else ()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Any:
							A						   =   FlaxGPTJModelTester(self      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   str:
							for model_class_name in self.all_model_classes:
								A   ,      A   ,      A						   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.check_use_cache_forward(A_  ,A_  ,A_  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Tuple:
							for model_class_name in self.all_model_classes:
								A   ,      A   ,      A						   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.check_use_cache_forward_with_attn_mask(
								    A_  ,A_  ,A_  ,A_      )


						@tooslow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Any:
							A						   =   GPTaTokenizer.from_pretrained('gpt2'  ,pad_token='<|endoftext|>'  ,padding_side='left'      )
							A						   =   tokenizer(['Hello this is a long string', 'Hey']  ,return_tensors='np'  ,padding=A_  ,truncation=A_      )

							A						   =   FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'      )
							A						   =   False
							A						   =   model.config.eos_token_id

							A						   =   jax.jit(model.generate      )

							A						   =   jit_generate(
							    inputs['input_ids']  ,attention_mask=inputs['attention_mask']  ,pad_token_id=tokenizer.pad_token_id      ).sequences

							A						   =   tokenizer.batch_decode(A_  ,skip_special_tokens=A_      )

							A						   =   [
							    '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(A_  ,A_      )


						@is_pt_flax_cross_test
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   List[Any]:
							A   ,      A						   =   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
									A						   =   self._prepare_for_class(A_  ,A_      )
									A						   =   {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

									# load corresponding PyTorch class
									A						   =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
									A						   =   getattr(A_  ,A_      )

									A   ,      A						   =   pt_inputs['input_ids'].shape
									A						   =   np.random.randint(0  ,seq_length - 1  ,size=(batch_size,)      )
									for batch_idx, start_index in enumerate(A_      ):
										A						   =   0
										A						   =   1
										A						   =   0
										A						   =   1
									A						   =   pt_model_class(A_      ).eval()
									A						   =   model_class(A_  ,dtype=jnp.floataa      )

									A						   =   convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,A_      )
									A						   =   fx_state

									with torch.no_grad():
										A						   =   pt_model(**A_      ).to_tuple()

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

									with tempfile.TemporaryDirectory() as tmpdirname:
										pt_model.save_pretrained(A_      )
										A						   =   model_class.from_pretrained(A_  ,from_pt=A_      )

									A						   =   fx_model_loaded(**A_      ).to_tuple()
									self.assertEqual(
									    len(A_      )  ,len(A_      )  ,'Output lengths differ between Flax and PyTorch'      )
									for fx_output_loaded, pt_output in zip(A_  ,A_      ):
										self.assert_almost_equals(fx_output_loaded[:, -1]  ,pt_output[:, -1].numpy()  ,4e-2      )


						@is_pt_flax_cross_test
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Dict:
							A   ,      A						   =   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
									A						   =   self._prepare_for_class(A_  ,A_      )
									A						   =   {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

									# load corresponding PyTorch class
									A						   =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
									A						   =   getattr(A_  ,A_      )

									A						   =   pt_model_class(A_      ).eval()
									A						   =   model_class(A_  ,dtype=jnp.floataa      )

									A						   =   load_flax_weights_in_pytorch_model(A_  ,fx_model.params      )
									A   ,      A						   =   pt_inputs['input_ids'].shape
									A						   =   np.random.randint(0  ,seq_length - 1  ,size=(batch_size,)      )
									for batch_idx, start_index in enumerate(A_      ):
										A						   =   0
										A						   =   1
										A						   =   0
										A						   =   1

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

									with torch.no_grad():
										A						   =   pt_model(**A_      ).to_tuple()

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

									with tempfile.TemporaryDirectory() as tmpdirname:
										fx_model.save_pretrained(A_      )
										A						   =   pt_model_class.from_pretrained(A_  ,from_flax=A_      )

									with torch.no_grad():
										A						   =   pt_model_loaded(**A_      ).to_tuple()

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




						@tooslow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   str:
							for model_class_name in self.all_model_classes:
								A						   =   model_class_name.from_pretrained('EleutherAI/gpt-j-6B'      )
								A						   =   model(np.ones((1, 1)      )      )
								self.assertIsNotNone(A_      )

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase							   =				{
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				['''CLIPTokenizerFast''']

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				['''CLIPFeatureExtractor''']
		_lowercase							   =				['''CLIPImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CLIPModel''',
		    '''CLIPPreTrainedModel''',
		    '''CLIPTextModel''',
		    '''CLIPTextModelWithProjection''',
		    '''CLIPVisionModel''',
		    '''CLIPVisionModelWithProjection''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFCLIPModel''',
		    '''TFCLIPPreTrainedModel''',
		    '''TFCLIPTextModel''',
		    '''TFCLIPVisionModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''FlaxCLIPModel''',
		    '''FlaxCLIPPreTrainedModel''',
		    '''FlaxCLIPTextModel''',
		    '''FlaxCLIPTextPreTrainedModel''',
		    '''FlaxCLIPVisionModel''',
		    '''FlaxCLIPVisionPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_clip import (
		    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPConfig,
		    CLIPOnnxConfig,
		    CLIPTextConfig,
		    CLIPVisionConfig,
		)
		from .processing_clip import CLIPProcessor
		from .tokenization_clip import CLIPTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_clip_fast import CLIPTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_clip import CLIPFeatureExtractor
				from .image_processing_clip import CLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clip import (
				    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPModel,
				    CLIPPreTrainedModel,
				    CLIPTextModel,
				    CLIPTextModelWithProjection,
				    CLIPVisionModel,
				    CLIPVisionModelWithProjection,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_clip import (
				    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCLIPModel,
				    TFCLIPPreTrainedModel,
				    TFCLIPTextModel,
				    TFCLIPVisionModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_clip import (
				    FlaxCLIPModel,
				    FlaxCLIPPreTrainedModel,
				    FlaxCLIPTextModel,
				    FlaxCLIPTextPreTrainedModel,
				    FlaxCLIPVisionModel,
				    FlaxCLIPVisionPreTrainedModel,
				)


else:
		import sys

		_lowercase							   =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)