


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
  a				:							Any					=			'platform'

  import jax
  import jax.numpy as jnp

  from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase=None ,     __lowerCamelCase=None       )		->Dict:


 '''simple docstring'''
 if attention_mask is None:
  snake_case   : str	  =      np.where(input_ids != config.pad_token_id ,     1 ,     0       )
 return {
     "input_ids": input_ids,
     "attention_mask": attention_mask,
 }

@require_flax
class      lowerCamelCase	:

       def __init__(			self	,						a__	,						a__=13	,						a__=7	,						a__=True	,						a__=False	,						a__=99	,						a__=16	,						a__=2	,						a__=4	,						a__=4	,						a__="gelu"	,						a__=0.1	,						a__=0.1	,						a__=20	,						a__=2	,						a__=1	,						a__=0	,						a__=16	,						a__=16	,						a__=0.02	,						):






        '''simple docstring'''






        snake_case   : int	  =      parent
        snake_case   : Any	  =      batch_size
        snake_case   : List[Any]	  =      seq_length
        snake_case   : Any	  =      is_training
        snake_case   : List[Any]	  =      use_labels
        snake_case   : int	  =      vocab_size
        snake_case   : Tuple	  =      hidden_size
        snake_case   : List[str]	  =      num_hidden_layers
        snake_case   : str	  =      num_attention_heads
        snake_case   : Optional[int]	  =      intermediate_size
        snake_case   : Tuple	  =      hidden_act
        snake_case   : List[str]	  =      hidden_dropout_prob
        snake_case   : Any	  =      attention_probs_dropout_prob
        snake_case   : Optional[int]	  =      max_position_embeddings
        snake_case   : str	  =      eos_token_id
        snake_case   : List[str]	  =      pad_token_id
        snake_case   : List[Any]	  =      bos_token_id
        snake_case   : List[Any]	  =      embed_dim
        snake_case   : Any	  =      word_embed_proj_dim
        snake_case   : List[Any]	  =      initializer_range
        snake_case   : Dict	  =      False

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






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

        snake_case   : str	  =      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=a__	,						word_embed_proj_dim=self.word_embed_proj_dim	,						initializer_range=self.initializer_range	,						use_cache=a__	,						)
        snake_case   : List[Any]	  =      prepare_opt_inputs_dict(a__	,						a__					)
        return config, inputs_dict

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case  ,					snake_case   : List[Any]	  =      self.prepare_config_and_inputs()
        return config, inputs_dict

       def 				UpperCAmelCase				(			self	,						a__	,						a__	,						a__					):






        '''simple docstring'''






        snake_case   : int	  =      20
        snake_case   : Any	  =      model_class_name(a__					)

        snake_case   : Dict	  =      inputs_dict['''input_ids''']
        snake_case   : Optional[Any]	  =      inputs_dict['''attention_mask''']

        snake_case   : int	  =      model.init_cache(input_ids.shape[0]	,						a__					)
        snake_case   : Tuple	  =      jnp.ones((input_ids.shape[0], max_length)	,						dtype='''i4'''					)

        snake_case   : Optional[int]	  =      jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1					)[None, :]	,						(input_ids.shape[0], input_ids.shape[-1] - 1)	,						)
        snake_case   : int	  =      model(
            input_ids[:, :-1]	,						attention_mask=a__	,						past_key_values=a__	,						position_ids=a__	,						)

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

        snake_case   : str	  =      model(a__					)

        snake_case   : str	  =      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 				UpperCAmelCase				(			self	,						a__	,						a__	,						a__					):






        '''simple docstring'''






        snake_case   : int	  =      20
        snake_case   : Dict	  =      model_class_name(a__					)

        snake_case  ,					snake_case   : Optional[int]	  =      (
            inputs_dict['''input_ids'''],
            inputs_dict['''attention_mask'''],
        )

        snake_case   : List[Any]	  =      jnp.concatenate(
            [
                attention_mask,
                jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])					),
            ]	,						axis=-1	,						)

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

        snake_case   : Any	  =      model(
            input_ids[:, :-1]	,						attention_mask=a__	,						past_key_values=a__	,						position_ids=a__	,						)
        snake_case   : int	  =      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,						dtype='''i4'''					)
        snake_case   : List[Any]	  =      model(
            input_ids[:, -1:]	,						past_key_values=outputs_cache.past_key_values	,						attention_mask=a__	,						position_ids=a__	,						)

        snake_case   : Union[str, Any]	  =      model(a__	,						attention_mask=a__					)

        snake_case   : Union[str, 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      lowerCamelCase	(  SCREAMING_SNAKE_CASE,			unittest.TestCase,			SCREAMING_SNAKE_CASE     ):
       _UpperCamelCase    		=      (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
       _UpperCamelCase    		=      () if is_flax_available() else ()

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : str	  =      FlaxOPTModelTester(self					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case  ,					snake_case   : Union[str, Any]	  =      self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(a__	,						a__	,						a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case  ,					snake_case   : List[Any]	  =      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(a__	,						a__	,						a__					)



       @slow
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
         snake_case   : List[str]	  =      model_class_name.from_pretrained('''facebook/opt-125m'''					)
         snake_case   : Optional[int]	  =      np.ones((1, 1)					) * model.config.eos_token_id
         snake_case   : Optional[int]	  =      model(a__					)
         self.assertIsNotNone(a__					)







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

       @slow
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Any	  =      FlaxOPTModel.from_pretrained('''facebook/opt-350m'''					)
        snake_case   : List[Any]	  =      jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]					)
        snake_case   : Optional[int]	  =      model(input_ids=a__					).last_hidden_state
        snake_case   : List[Any]	  =      (1, 11, 5_12)
        self.assertEqual(output.shape	,						a__					)
        snake_case   : Optional[int]	  =      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]	,						a__	,						atol=4e-2					)					)







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

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        super().setUp()
        snake_case   : Union[str, Any]	  =      '''facebook/opt-350m'''



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Union[str, Any]	  =      FlaxOPTForCausalLM.from_pretrained(self.path_model					)
        snake_case   : List[Any]	  =      GPTaTokenizer.from_pretrained(self.path_model					)

        snake_case   : List[Any]	  =      [
            '''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
        snake_case   : List[str]	  =      tokenizer(a__	,						return_tensors='''jax'''	,						padding=a__	,						add_special_tokens=a__					)
        snake_case   : Optional[Any]	  =      model(inputs.input_ids	,						attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
        snake_case   : Tuple	  =      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(a__	,						a__	,						atol=4e-2					)					)

        snake_case   : Tuple	  =      jax.jit(a__					)
        snake_case   : Dict	  =      model(inputs.input_ids	,						attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
        self.assertTrue(jnp.allclose(a__	,						a__	,						atol=4e-2					)					)







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

       @property
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        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 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Optional[int]	  =      '''facebook/opt-125m'''

        snake_case   : List[str]	  =      [
            '''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''',
        ]

        snake_case   : Optional[int]	  =      []

        snake_case   : Any	  =      FlaxOPTForCausalLM.from_pretrained(a__					)
        snake_case   : Optional[int]	  =      GPTaTokenizer.from_pretrained(a__					)

        for prompt in self.prompts:
         snake_case   : Dict	  =      tokenizer(a__	,						return_tensors='''jax'''					).input_ids

         snake_case   : Optional[Any]	  =      model.generate(a__	,						max_length=10					)
         snake_case   : Union[str, Any]	  =      generated_ids[0]

         snake_case   : Optional[Any]	  =      tokenizer.batch_decode(a__	,						skip_special_tokens=a__					)
         predicted_outputs += generated_string

        self.assertListEqual(a__	,						a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Union[str, Any]	  =      '''facebook/opt-350m'''

        snake_case   : int	  =      [
            '''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''',
        ]

        snake_case   : Dict	  =      []
        snake_case   : int	  =      FlaxOPTForCausalLM.from_pretrained(a__					)
        snake_case   : List[str]	  =      GPTaTokenizer.from_pretrained(a__					)

        for prompt in self.prompts:
         snake_case   : str	  =      tokenizer(a__	,						return_tensors='''jax'''					).input_ids

         snake_case   : List[Any]	  =      model.generate(a__	,						max_length=10					)
         snake_case   : Optional[Any]	  =      generated_ids[0]

         snake_case   : Dict	  =      tokenizer.batch_decode(a__	,						skip_special_tokens=a__					)
         predicted_outputs += generated_string

        self.assertListEqual(a__	,						a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Union[str, Any]	  =      '''facebook/opt-125m'''
        snake_case   : int	  =      [
            '''Today is a beautiful day and I want to thank''',
            '''In the city of Rome Canaver Canaver Canaver Canaver''',
        ]
        snake_case   : List[Any]	  =      FlaxOPTForCausalLM.from_pretrained(a__					)
        snake_case   : List[Any]	  =      GPTaTokenizer.from_pretrained(a__					)
        snake_case   : List[str]	  =      tokenizer(
            [
                '''Today is a beautiful day and I want to''',
                '''In the city of''',
            ]	,						return_tensors='''jax'''	,						padding=a__	,						)

        snake_case   : Tuple	  =      jax.jit(model.generate					)

        snake_case   : Optional[int]	  =      jit_generate(inputs['''input_ids''']	,						attention_mask=inputs['''attention_mask''']					).sequences

        snake_case   : Optional[int]	  =      tokenizer.batch_decode(a__	,						skip_special_tokens=a__					)

        self.assertIsNotNone(a__	,						a__					)



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Dict	  =      '''facebook/opt-350m'''

        snake_case   : Dict	  =      GPTaTokenizer.from_pretrained(a__					)
        snake_case   : Optional[int]	  =      FlaxOPTForCausalLM.from_pretrained(a__					)

        snake_case   : Optional[int]	  =      '''left'''

        # use different length sentences to test batching
        snake_case   : Union[str, Any]	  =      [
            '''Hello, my dog is a little''',
            '''Today, I''',
        ]

        snake_case   : Dict	  =      tokenizer(a__	,						return_tensors='''jax'''	,						padding=a__					)
        snake_case   : List[str]	  =      inputs['''input_ids''']

        snake_case   : str	  =      model.generate(input_ids=a__	,						attention_mask=inputs['''attention_mask''']	,						trace=a__					)

        snake_case   : Union[str, Any]	  =      tokenizer(sentences[0]	,						return_tensors='''jax'''					).input_ids
        snake_case   : List[Any]	  =      model.generate(input_ids=a__					)

        snake_case   : Dict	  =      inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
        snake_case   : Dict	  =      tokenizer(sentences[1]	,						return_tensors='''jax'''					).input_ids
        snake_case   : List[str]	  =      model.generate(input_ids=a__	,						max_length=model.config.max_length - num_paddings					)

        snake_case   : int	  =      tokenizer.batch_decode(outputs[0]	,						skip_special_tokens=a__					)
        snake_case   : str	  =      tokenizer.decode(output_non_padded[0][0]	,						skip_special_tokens=a__					)
        snake_case   : Dict	  =      tokenizer.decode(output_padded[0][0]	,						skip_special_tokens=a__					)

        snake_case   : Tuple	  =      [
            '''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(a__	,						a__					)
        self.assertListEqual(a__	,						[non_padded_sentence, padded_sentence]					)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a				:							List[str]					=			{
    'configuration_donut_swin': ['DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DonutSwinConfig'],
    'processing_donut': ['DonutProcessor'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							Dict					=			[
      'DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DonutSwinModel',
      'DonutSwinPreTrainedModel',
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							Any					=			['DonutFeatureExtractor']
  a				:							Dict					=			['DonutImageProcessor']


if TYPE_CHECKING:
  from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
  from .processing_donut import DonutProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_donut_swin import (
        DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
        DonutSwinModel,
        DonutSwinPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_donut import DonutFeatureExtractor
    from .image_processing_donut import DonutImageProcessor

else:
  import sys

  a				:							Union[str, Any]					=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






