



"""simple docstring"""







import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
							import torch

							from ..models.auto.modeling_auto import (
							    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
							    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
							)

if is_tf_available() and is_tensorflow_probability_available():
							import tensorflow as tf
							import tensorflow_probability as tfp

							from ..models.auto.modeling_tf_auto import (
							    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
							    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
							)






class   UpperCAmelCase__		(   UpperCamelCase  ):



		def __call__(			self	:     int   ,				snake_case	:     int=None   ,				snake_case	:     Tuple=None   ,				**snake_case	:     str					)      ->    Optional[Any]:






									'''simple docstring'''

									requires_backends(self   ,				'pandas'					)
									import pandas as pd

									if table is None:
																raise ValueError('Keyword argument `table` cannot be None.'					)
									elif query is None:
																if isinstance(a__   ,				a__					) and table.get('query'					) is not None and table.get('table'					) is not None:
																							A    =       [table]
																elif isinstance(a__   ,				a__					) and len(a__					) > 0:
																							if not all(isinstance(a__   ,				a__					) for d in table					):
																														raise ValueError(
																														    f"""Keyword argument `table` should be a list of dict, but is {(type(a__					) for d in table)}"""					)

																							if table[0].get('query'					) is not None and table[0].get('table'					) is not None:
																														A    =       table
																							else:
																														raise ValueError(
																														    'If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`'
																														    f""" and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys."""					)
																elif Dataset is not None and isinstance(a__   ,				a__					) or isinstance(a__   ,				types.GeneratorType					):
																							return table
																else:
																							raise ValueError(
																							    'Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but '
																							    f"""is {type(a__					)})"""					)
									else:
																A    =       [{'''table''': table, '''query''': query}]

									for tqa_pipeline_input in tqa_pipeline_inputs:
																if not isinstance(tqa_pipeline_input['table']   ,				pd.DataFrame					):
																							if tqa_pipeline_input["table"] is None:
																														raise ValueError('Table cannot be None.'					)

																							A    =       pd.DataFrame(tqa_pipeline_input['table']					)

									return tqa_pipeline_inputs






@add_end_docstrings(UpperCamelCase  )
class   UpperCAmelCase__		(   UpperCamelCase  ):
		lowerCAmelCase_							:						List[Any]							=  """table,query"""



		def __init__(			self	:     int   ,				snake_case	:     Union[str, Any]=TableQuestionAnsweringArgumentHandler()   ,				*snake_case	:     Optional[Any]   ,				**snake_case	:     int					)      ->    int:






									'''simple docstring'''

									super().__init__(*a__   ,				**a__					)
									A    =       args_parser

									self.check_model_type(
									    dict(
									        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
									        + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()					)
									    if self.framework == 'tf'
									    else dict(
									        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()					)					)

									A    =       bool(getattr(self.model.config   ,				'aggregation_labels'   ,				a__					)					) and bool(
									    getattr(self.model.config   ,				'num_aggregation_labels'   ,				a__					)					)
									A    =       '''tapas''' if hasattr(self.model.config   ,				'aggregation_labels'					) else None



		def        A_		(			self	:     Optional[Any]   ,				**snake_case	:     Tuple					)      ->    Dict:






									'''simple docstring'''

									return self.model(**a__					)



		def        A_		(			self	:     Optional[Any]   ,				**snake_case	:     str					)      ->    Any:






									'''simple docstring'''

									if self.framework == "pt":
																A    =       []
																A    =       []
																A    =       None
																A    =       inputs['''input_ids'''].shape[0]

																A    =       inputs['''input_ids'''].to(self.device					)
																A    =       inputs['''attention_mask'''].to(self.device					)
																A    =       inputs['''token_type_ids'''].to(self.device					)
																A    =       None

																for index in range(a__					):
																							# If sequences have already been processed, the token type IDs will be created according to the previous
																							# answer.
																							if prev_answers is not None:
																														A    =       token_type_ids_example[:, 3]  # shape (seq_len,)
																														A    =       np.zeros_like(prev_labels_example.cpu().numpy()					)  # shape (seq_len,)

																														A    =       token_type_ids[index]  # shape (seq_len, 7)
																														for i in range(model_labels.shape[0]					):
																																					A    =       token_type_ids_example[:, 0].tolist()[i]
																																					A    =       token_type_ids_example[:, 1].tolist()[i] - 1
																																					A    =       token_type_ids_example[:, 2].tolist()[i] - 1

																																					if row_id >= 0 and col_id >= 0 and segment_id == 1:
																																												A    =       int(prev_answers[(col_id, row_id)]					)

																														A    =       torch.from_numpy(a__					).type(torch.long					).to(self.device					)

																							A    =       input_ids[index]
																							A    =       attention_mask[index]  # shape (seq_len,)
																							A    =       token_type_ids[index]  # shape (seq_len, 7)
																							A    =       self.model(
																							    input_ids=input_ids_example.unsqueeze(0					)   ,				attention_mask=attention_mask_example.unsqueeze(0					)   ,				token_type_ids=token_type_ids_example.unsqueeze(0					)   ,				)
																							A    =       outputs.logits

																							if self.aggregate:
																														all_aggregations.append(outputs.logits_aggregation					)

																							all_logits.append(a__					)

																							A    =       torch.distributions.Bernoulli(logits=a__					)
																							A    =       dist_per_token.probs * attention_mask_example.type(torch.floataa					).to(
																							    dist_per_token.probs.device					)

																							A    =       collections.defaultdict(a__					)
																							for i, p in enumerate(probabilities.squeeze().tolist()					):
																														A    =       token_type_ids_example[:, 0].tolist()[i]
																														A    =       token_type_ids_example[:, 1].tolist()[i] - 1
																														A    =       token_type_ids_example[:, 2].tolist()[i] - 1
																														if col >= 0 and row >= 0 and segment_id == 1:
																																					coords_to_probs[(col, row)].append(a__					)

																							A    =       {key: np.array(coords_to_probs[key]					).mean() > 0.5 for key in coords_to_probs}

																A    =       torch.cat(tuple(a__					)   ,				0					)

																return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(a__					)   ,				0					))
									else:
																A    =       []
																A    =       []
																A    =       None
																A    =       inputs['''input_ids'''].shape[0]

																A    =       inputs['''input_ids''']
																A    =       inputs['''attention_mask''']
																A    =       inputs['''token_type_ids'''].numpy()
																A    =       None

																for index in range(a__					):
																							# If sequences have already been processed, the token type IDs will be created according to the previous
																							# answer.
																							if prev_answers is not None:
																														A    =       token_type_ids_example[:, 3]  # shape (seq_len,)
																														A    =       np.zeros_like(a__   ,				dtype=np.intaa					)  # shape (seq_len,)

																														A    =       token_type_ids[index]  # shape (seq_len, 7)
																														for i in range(model_labels.shape[0]					):
																																					A    =       token_type_ids_example[:, 0].tolist()[i]
																																					A    =       token_type_ids_example[:, 1].tolist()[i] - 1
																																					A    =       token_type_ids_example[:, 2].tolist()[i] - 1

																																					if row_id >= 0 and col_id >= 0 and segment_id == 1:
																																												A    =       int(prev_answers[(col_id, row_id)]					)

																														A    =       model_labels

																							A    =       input_ids[index]
																							A    =       attention_mask[index]  # shape (seq_len,)
																							A    =       token_type_ids[index]  # shape (seq_len, 7)
																							A    =       self.model(
																							    input_ids=np.expand_dims(a__   ,				axis=0					)   ,				attention_mask=np.expand_dims(a__   ,				axis=0					)   ,				token_type_ids=np.expand_dims(a__   ,				axis=0					)   ,				)
																							A    =       outputs.logits

																							if self.aggregate:
																														all_aggregations.append(outputs.logits_aggregation					)

																							all_logits.append(a__					)

																							A    =       tfp.distributions.Bernoulli(logits=a__					)
																							A    =       dist_per_token.probs_parameter() * tf.cast(a__   ,				tf.floataa					)

																							A    =       collections.defaultdict(a__					)
																							A    =       token_type_ids_example
																							for i, p in enumerate(tf.squeeze(a__					).numpy().tolist()					):
																														A    =       token_type_ids_example[:, 0].tolist()[i]
																														A    =       token_type_ids_example[:, 1].tolist()[i] - 1
																														A    =       token_type_ids_example[:, 2].tolist()[i] - 1
																														if col >= 0 and row >= 0 and segment_id == 1:
																																					coords_to_probs[(col, row)].append(a__					)

																							A    =       {key: np.array(coords_to_probs[key]					).mean() > 0.5 for key in coords_to_probs}

																A    =       tf.concat(tuple(a__					)   ,				0					)

																return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(a__					)   ,				0					))



		def __call__(			self	:     str   ,				*snake_case	:     Any   ,				**snake_case	:     Union[str, Any]					)      ->    List[str]:






									'''simple docstring'''

									A    =       self._args_parser(*a__   ,				**a__					)

									A    =       super().__call__(a__   ,				**a__					)
									if len(a__					) == 1:
																return results[0]
									return results



		def        A_		(			self	:     int   ,				snake_case	:     int=None   ,				snake_case	:     int=None   ,				snake_case	:     Tuple=None   ,				**snake_case	:     Any					)      ->    int:






									'''simple docstring'''

									A    =       {}
									if padding is not None:
																A    =       padding
									if truncation is not None:
																A    =       truncation

									A    =       {}
									if sequential is not None:
																A    =       sequential
									return preprocess_params, forward_params, {}



		def        A_		(			self	:     Tuple   ,				snake_case	:     List[Any]   ,				snake_case	:     List[str]=None   ,				snake_case	:     Optional[Any]=True   ,				snake_case	:     int=None					)      ->    Optional[Any]:






									'''simple docstring'''

									if truncation is None:
																if self.type == "tapas":
																							A    =       '''drop_rows_to_fit'''
																else:
																							A    =       '''do_not_truncate'''

									A    =       pipeline_input['''table'''], pipeline_input['''query''']
									if table.empty:
																raise ValueError('table is empty'					)
									if query is None or query == "":
																raise ValueError('query is empty'					)
									A    =       self.tokenizer(a__   ,				a__   ,				return_tensors=self.framework   ,				truncation=a__   ,				padding=a__					)
									A    =       table
									return inputs



		def        A_		(			self	:     Dict   ,				snake_case	:     Tuple   ,				snake_case	:     Dict=False					)      ->    Tuple:






									'''simple docstring'''

									A    =       model_inputs.pop('table'					)

									if self.type == "tapas":
																if sequential:
																							A    =       self.sequential_inference(**a__					)
																else:
																							A    =       self.batch_inference(**a__					)
									else:
																A    =       self.model.generate(**a__					)
									A    =       {'''model_inputs''': model_inputs, '''table''': table, '''outputs''': outputs}
									return model_outputs



		def        A_		(			self	:     Tuple   ,				snake_case	:     Any					)      ->    Any:






									'''simple docstring'''

									A    =       model_outputs['''model_inputs''']
									A    =       model_outputs['''table''']
									A    =       model_outputs['''outputs''']
									if self.type == "tapas":
																if self.aggregate:
																							A    =       outputs[:2]
																							A    =       self.tokenizer.convert_logits_to_predictions(a__   ,				a__   ,				a__					)
																							A    =       predictions
																							A    =       {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(a__					)}

																							A    =       self.model.config.no_aggregation_label_index
																							A    =       {
																							    i: aggregators[i] + ''' > ''' for i, pred in enumerate(a__					) if pred != no_agg_label_index
																							}
																else:
																							A    =       outputs[0]
																							A    =       self.tokenizer.convert_logits_to_predictions(a__   ,				a__					)
																							A    =       predictions[0]
																							A    =       {}
																							A    =       {}
																A    =       []
																for index, coordinates in enumerate(a__					):
																							A    =       [table.iat[coordinate] for coordinate in coordinates]
																							A    =       aggregators.get(a__   ,				''					)
																							A    =       aggregators_prefix.get(a__   ,				''					)
																							A    =       {
																							    '''answer''': aggregator_prefix + ''', '''.join(a__					),
																							    '''coordinates''': coordinates,
																							    '''cells''': [table.iat[coordinate] for coordinate in coordinates],
																							}
																							if aggregator:
																														A    =       aggregator

																							answers.append(a__					)
																if len(a__					) == 0:
																							raise PipelineException('Empty answer'					)
									else:
																A    =       [{'''answer''': answer} for answer in self.tokenizer.batch_decode(a__   ,				skip_special_tokens=a__					)]

									return answers if len(a__					) > 1 else answers[0]







import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

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


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

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
  import torch

@require_flax
class   lowerCamelCase						:

       def __init__(      self						,    a__						,    a__=14						,    a__=7						,    a__=True						,    a__=True						,    a__=True						,    a__=99						,    a__=32						,    a__=5						,    a__=4						,    a__=37						,    a__="gelu"						,    a__=0.1						,    a__=0.1						,    a__=5_12						,    a__=0.02						,    a__=None						,    ):






        '''simple docstring'''






        snake_case :			Any							=   parent
        snake_case :			str							=   batch_size
        snake_case :			str							=   seq_length
        snake_case :			str							=   is_training
        snake_case :			List[str]							=   use_input_mask
        snake_case :			str							=   use_labels
        snake_case :			Union[str, Any]							=   vocab_size
        snake_case :			Dict							=   d_model
        snake_case :			List[str]							=   num_hidden_layers
        snake_case :			Optional[int]							=   num_attention_heads
        snake_case :			Optional[Any]							=   ffn_dim
        snake_case :			Dict							=   activation_function
        snake_case :			List[str]							=   activation_dropout
        snake_case :			Any							=   attention_dropout
        snake_case :			str							=   max_position_embeddings
        snake_case :			Optional[int]							=   initializer_range
        snake_case :			Union[str, Any]							=   None
        snake_case :			int							=   0
        snake_case :			int							=   2
        snake_case :			Union[str, Any]							=   1

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Any							=   np.clip(ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size       )						,    3						,    self.vocab_size       )

        snake_case :			Union[str, Any]							=   None
        if self.use_input_mask:
         snake_case :			Tuple							=   random_attention_mask([self.batch_size, self.seq_length]       )

        snake_case :			Optional[Any]							=   XGLMConfig(
            vocab_size=self.vocab_size						,    d_model=self.hidden_size						,    num_layers=self.num_hidden_layers						,    attention_heads=self.num_attention_heads						,    ffn_dim=self.ffn_dim						,    activation_function=self.activation_function						,    activation_dropout=self.activation_dropout						,    attention_dropout=self.attention_dropout						,    max_position_embeddings=self.max_position_embeddings						,    initializer_range=self.initializer_range						,    use_cache=a__						,    bos_token_id=self.bos_token_id						,    eos_token_id=self.eos_token_id						,    pad_token_id=self.pad_token_id						,    )

        return (config, input_ids, input_mask)

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   self.prepare_config_and_inputs()
        snake_case	,       snake_case	,       snake_case :			List[str]							=   config_and_inputs
        snake_case :			List[str]							=   {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case	,       snake_case	,       snake_case :			List[str]							=   self.prepare_config_and_inputs()

        snake_case :			Tuple							=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
        snake_case :			Union[str, Any]							=   ids_tensor([self.batch_size, self.seq_length]						,    vocab_size=2       )

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
        )

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






        '''simple docstring'''






        snake_case :			Optional[Any]							=   20
        snake_case :			List[Any]							=   model_class_name(a__       )

        snake_case :			List[Any]							=   model.init_cache(input_ids.shape[0]						,    a__       )
        snake_case :			Optional[int]							=   jnp.ones((input_ids.shape[0], max_decoder_length)						,    dtype='''i4'''       )

        snake_case :			Optional[Any]							=   jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1       )[None, :]						,    (input_ids.shape[0], input_ids.shape[-1] - 1)       )
        snake_case :			Tuple							=   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 :			Dict							=   model(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}'''       )



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






        '''simple docstring'''






        snake_case :			Optional[int]							=   20
        snake_case :			Dict							=   model_class_name(a__       )

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

        snake_case :			List[str]							=   model.init_cache(input_ids.shape[0]						,    a__       )
        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 :			Optional[int]							=   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 :			Optional[int]							=   model(
            input_ids[:, -1:]						,    past_key_values=outputs_cache.past_key_values						,    attention_mask=a__						,    position_ids=a__						,    )

        snake_case :			Optional[Any]							=   model(a__						,    attention_mask=a__       )
        snake_case :			Optional[int]							=   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_sentencepiece
@require_flax
class   lowerCamelCase						(	SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      unittest.TestCase  ):
       _UpperCamelCase     		=   (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
       _UpperCamelCase     		=   (FlaxXGLMForCausalLM,) if is_flax_available() else ()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Any							=   FlaxXGLMModelTester(self       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
         snake_case	,       snake_case	,       snake_case :			str							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward(a__						,    a__						,    a__						,    a__       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
         snake_case	,       snake_case	,       snake_case :			int							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             a__						,    a__						,    a__						,    a__       )

       @slow
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Any							=   XGLMTokenizer.from_pretrained('''XGLM'''						,    padding_side='''left'''       )
        snake_case :			Optional[int]							=   tokenizer(['''Hello this is a long string''', '''Hey''']						,    return_tensors='''np'''						,    padding=a__						,    truncation=a__       )

        snake_case :			List[Any]							=   FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''       )
        snake_case :			Optional[int]							=   1
        snake_case :			str							=   False

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

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

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

        snake_case :			Tuple							=   [
            '''Hello this is a long string of questions, but I\'m not sure if I\'m''',
            '''Hey, I\'m a newbie to the forum and I\'''',
        ]

        self.assertListEqual(a__						,    a__       )

       @is_pt_flax_cross_test
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case	,       snake_case :			Union[str, Any]							=   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
          snake_case :			Union[str, Any]							=   self._prepare_for_class(a__						,    a__       )
          snake_case :			int							=   {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

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

          snake_case	,       snake_case :			Optional[Any]							=   pt_inputs['''input_ids'''].shape
          snake_case :			Tuple							=   np.random.randint(0						,    seq_length - 1						,    size=(batch_size,)       )
          for batch_idx, start_index in enumerate(a__       ):
           snake_case :			Optional[Any]							=   0
           snake_case :			Optional[int]							=   1
           snake_case :			Optional[Any]							=   0
           snake_case :			Optional[Any]							=   1
          snake_case :			Dict							=   pt_model_class(a__       ).eval()
          # Flax models don't use the `use_cache` option and cache is not returned as a default.
          # So we disable `use_cache` here for PyTorch model.
          snake_case :			Any							=   False
          snake_case :			str							=   model_class(a__						,    dtype=jnp.floataa       )
          snake_case :			Optional[int]							=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,    a__       )
          snake_case :			Optional[int]							=   fx_state

          with torch.no_grad():
           snake_case :			int							=   pt_model(**a__       ).to_tuple()

          snake_case :			Optional[int]							=   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__       )
           snake_case :			Union[str, Any]							=   model_class.from_pretrained(a__						,    from_pt=a__       )

          snake_case :			List[Any]							=   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 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case	,       snake_case :			List[Any]							=   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
          snake_case :			str							=   self._prepare_for_class(a__						,    a__       )
          snake_case :			List[Any]							=   {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

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

          snake_case :			List[str]							=   pt_model_class(a__       ).eval()
          snake_case :			Tuple							=   False
          snake_case :			Optional[int]							=   model_class(a__						,    dtype=jnp.floataa       )

          snake_case :			Any							=   load_flax_weights_in_pytorch_model(a__						,    fx_model.params       )
          snake_case	,       snake_case :			Union[str, Any]							=   pt_inputs['''input_ids'''].shape
          snake_case :			List[Any]							=   np.random.randint(0						,    seq_length - 1						,    size=(batch_size,)       )
          for batch_idx, start_index in enumerate(a__       ):
           snake_case :			Union[str, Any]							=   0
           snake_case :			Tuple							=   1
           snake_case :			int							=   0
           snake_case :			Optional[int]							=   1

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

          with torch.no_grad():
           snake_case :			Optional[int]							=   pt_model(**a__       ).to_tuple()

          snake_case :			Any							=   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__       )
           snake_case :			int							=   pt_model_class.from_pretrained(a__						,    from_flax=a__       )

          with torch.no_grad():
           snake_case :			int							=   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       )



       @slow
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
         snake_case :			str							=   model_class_name.from_pretrained('''facebook/xglm-564M'''       )
         snake_case :			List[Any]							=   model(np.ones((1, 1)       )       )
         self.assertIsNotNone(a__       )






