


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, 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			=						'platform'

			import jax
			import jax.numpy as jnp

			from transformers import BlenderbotTokenizer
			from transformers.models.blenderbot.modeling_flax_blenderbot import (
			    FlaxBlenderbotForConditionalGeneration,
			    FlaxBlenderbotModel,
			    shift_tokens_right,
			)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : int=None      ,					SCREAMING_SNAKE_CASE__      : Dict=None      ,					SCREAMING_SNAKE_CASE__      : List[Any]=None      ,					SCREAMING_SNAKE_CASE__      : Any=None      ,					SCREAMING_SNAKE_CASE__      : Any=None      ,					SCREAMING_SNAKE_CASE__      : Optional[int]=None      ,					):




					if attention_mask is None:
										__UpperCamelCase   							=np.where(input_ids != config.pad_token_id      ,					1      ,					0    )
					if decoder_attention_mask is None:
										__UpperCamelCase   							=np.where(decoder_input_ids != config.pad_token_id      ,					1      ,					0    )
					if head_mask is None:
										__UpperCamelCase   							=np.ones((config.encoder_layers, config.encoder_attention_heads)    )
					if decoder_head_mask is None:
										__UpperCamelCase   							=np.ones((config.decoder_layers, config.decoder_attention_heads)    )
					if cross_attn_head_mask is None:
										__UpperCamelCase   							=np.ones((config.decoder_layers, config.decoder_attention_heads)    )
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": attention_mask,
					}






class    UpperCAmelCase__	:



				"""simple docstring"""






				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_=32 ,       A_=2 ,       A_=1 ,       A_=0 ,       A_=0.02 ,       )  ->	Optional[int]:
									__UpperCamelCase   							=parent
									__UpperCamelCase   							=batch_size
									__UpperCamelCase   							=seq_length
									__UpperCamelCase   							=is_training
									__UpperCamelCase   							=use_labels
									__UpperCamelCase   							=vocab_size
									__UpperCamelCase   							=hidden_size
									__UpperCamelCase   							=num_hidden_layers
									__UpperCamelCase   							=num_attention_heads
									__UpperCamelCase   							=intermediate_size
									__UpperCamelCase   							=hidden_act
									__UpperCamelCase   							=hidden_dropout_prob
									__UpperCamelCase   							=attention_probs_dropout_prob
									__UpperCamelCase   							=max_position_embeddings
									__UpperCamelCase   							=eos_token_id
									__UpperCamelCase   							=pad_token_id
									__UpperCamelCase   							=bos_token_id
									__UpperCamelCase   							=initializer_range






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

									__UpperCamelCase   							=shift_tokens_right(UpperCamelCase_ ,       1 ,       2   )

									__UpperCamelCase   							=BlenderbotConfig(
									    vocab_size=self.vocab_size ,       d_model=self.hidden_size ,       encoder_layers=self.num_hidden_layers ,       decoder_layers=self.num_hidden_layers ,       encoder_attention_heads=self.num_attention_heads ,       decoder_attention_heads=self.num_attention_heads ,       encoder_ffn_dim=self.intermediate_size ,       decoder_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 ,       initializer_range=self.initializer_range ,       use_cache=UpperCamelCase_ ,       )
									__UpperCamelCase   							=prepare_blenderbot_inputs_dict(UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_   )
									return config, inputs_dict






				def _a (      self   )  ->	Optional[Any]:
									__UpperCamelCase							,				__UpperCamelCase   							=self.prepare_config_and_inputs()
									return config, inputs_dict






				def _a (      self ,       A_ ,       A_ ,       A_   )  ->	int:
									__UpperCamelCase   							=20
									__UpperCamelCase   							=model_class_name(UpperCamelCase_   )

									__UpperCamelCase   							=model.encode(inputs_dict['input_ids']   )

									__UpperCamelCase							,				__UpperCamelCase   							=(
									    inputs_dict['decoder_input_ids'],
									    inputs_dict['decoder_attention_mask'],
									)

									__UpperCamelCase   							=model.init_cache(decoder_input_ids.shape[0] ,       UpperCamelCase_ ,       UpperCamelCase_   )
									__UpperCamelCase   							=jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,       dtype='i4'   )

									__UpperCamelCase   							=jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :] ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,       )
									__UpperCamelCase   							=model.decode(
									    decoder_input_ids[:, :-1] ,       UpperCamelCase_ ,       decoder_attention_mask=UpperCamelCase_ ,       past_key_values=UpperCamelCase_ ,       decoder_position_ids=UpperCamelCase_ ,       )

									__UpperCamelCase   							=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,       dtype='i4'   )
									__UpperCamelCase   							=model.decode(
									    decoder_input_ids[:, -1:] ,       UpperCamelCase_ ,       decoder_attention_mask=UpperCamelCase_ ,       past_key_values=outputs_cache.past_key_values ,       decoder_position_ids=UpperCamelCase_ ,       )

									__UpperCamelCase   							=model.decode(UpperCamelCase_ ,       UpperCamelCase_   )

									__UpperCamelCase   							=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 _a (      self ,       A_ ,       A_ ,       A_   )  ->	Any:
									__UpperCamelCase   							=20
									__UpperCamelCase   							=model_class_name(UpperCamelCase_   )

									__UpperCamelCase   							=model.encode(inputs_dict['input_ids']   )

									__UpperCamelCase							,				__UpperCamelCase   							=(
									    inputs_dict['decoder_input_ids'],
									    inputs_dict['decoder_attention_mask'],
									)

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

									__UpperCamelCase   							=model.init_cache(decoder_input_ids.shape[0] ,       UpperCamelCase_ ,       UpperCamelCase_   )
									__UpperCamelCase   							=jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :] ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,       )

									__UpperCamelCase   							=model.decode(
									    decoder_input_ids[:, :-1] ,       UpperCamelCase_ ,       decoder_attention_mask=UpperCamelCase_ ,       past_key_values=UpperCamelCase_ ,       decoder_position_ids=UpperCamelCase_ ,       )
									__UpperCamelCase   							=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,       dtype='i4'   )
									__UpperCamelCase   							=model.decode(
									    decoder_input_ids[:, -1:] ,       UpperCamelCase_ ,       past_key_values=outputs_cache.past_key_values ,       decoder_attention_mask=UpperCamelCase_ ,       decoder_position_ids=UpperCamelCase_ ,       )

									__UpperCamelCase   							=model.decode(UpperCamelCase_ ,       UpperCamelCase_ ,       decoder_attention_mask=UpperCamelCase_   )

									__UpperCamelCase   							=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    UpperCAmelCase__	( unittest.TestCase		):



				"""simple docstring"""






				UpperCAmelCase__						:		List[Any]              =  9_9






				def _a (      self   )  ->	Optional[Any]:
									__UpperCamelCase   							=np.array(
									    [
									        [71, 82, 18, 33, 46, 91, 2],
									        [68, 34, 26, 58, 30, 82, 2],
									        [5, 97, 17, 39, 94, 40, 2],
									        [76, 83, 94, 25, 70, 78, 2],
									        [87, 59, 41, 35, 48, 66, 2],
									        [55, 13, 16, 58, 5, 2, 1],  # note padding
									        [64, 27, 31, 51, 12, 75, 2],
									        [52, 64, 86, 17, 83, 39, 2],
									        [48, 61, 9, 24, 71, 82, 2],
									        [26, 1, 60, 48, 22, 13, 2],
									        [21, 5, 62, 28, 14, 76, 2],
									        [45, 98, 37, 86, 59, 48, 2],
									        [70, 70, 50, 9, 28, 0, 2],
									    ] ,       dtype=np.intaa ,       )

									__UpperCamelCase   							=input_ids.shape[0]
									__UpperCamelCase   							=BlenderbotConfig(
									    vocab_size=self.vocab_size ,       d_model=24 ,       encoder_layers=2 ,       decoder_layers=2 ,       encoder_attention_heads=2 ,       decoder_attention_heads=2 ,       encoder_ffn_dim=32 ,       decoder_ffn_dim=32 ,       max_position_embeddings=48 ,       eos_token_id=2 ,       pad_token_id=1 ,       bos_token_id=0 ,       )
									return config, input_ids, batch_size






				def _a (      self   )  ->	int:
									__UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase   							=self._get_config_and_data()
									__UpperCamelCase   							=FlaxBlenderbotForConditionalGeneration(UpperCamelCase_   )
									__UpperCamelCase   							=lm_model(input_ids=UpperCamelCase_   )
									__UpperCamelCase   							=(batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs['logits'].shape ,       UpperCamelCase_   )






				def _a (      self   )  ->	List[str]:
									__UpperCamelCase   							=BlenderbotConfig(
									    vocab_size=self.vocab_size ,       d_model=14 ,       encoder_layers=2 ,       decoder_layers=2 ,       encoder_attention_heads=2 ,       decoder_attention_heads=2 ,       encoder_ffn_dim=8 ,       decoder_ffn_dim=8 ,       max_position_embeddings=48 ,       )
									__UpperCamelCase   							=FlaxBlenderbotForConditionalGeneration(UpperCamelCase_   )
									__UpperCamelCase   							=np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] ,       dtype=np.intaa   )
									__UpperCamelCase   							=np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] ,       dtype=np.intaa   )
									__UpperCamelCase   							=lm_model(input_ids=UpperCamelCase_ ,       decoder_input_ids=UpperCamelCase_   )
									__UpperCamelCase   							=(*summary.shape, config.vocab_size)
									self.assertEqual(outputs['logits'].shape ,       UpperCamelCase_   )






				def _a (      self   )  ->	str:
									__UpperCamelCase   							=np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] ,       dtype=np.intaa   )
									__UpperCamelCase   							=shift_tokens_right(UpperCamelCase_ ,       1 ,       2   )
									__UpperCamelCase   							=np.equal(UpperCamelCase_ ,       1   ).astype(np.floataa   ).sum()
									__UpperCamelCase   							=np.equal(UpperCamelCase_ ,       1   ).astype(np.floataa   ).sum()
									self.assertEqual(shifted.shape ,       input_ids.shape   )
									self.assertEqual(UpperCamelCase_ ,       n_pad_before - 1   )
									self.assertTrue(np.equal(shifted[:, 0] ,       2   ).all()   )






@require_flax
class    UpperCAmelCase__	( UpperCamelCase__       ,  unittest.TestCase       ,  UpperCamelCase__		):



				"""simple docstring"""






				UpperCAmelCase__						:		List[str]              =  True
				UpperCAmelCase__						:		List[Any]              =  (
				    (
				        FlaxBlenderbotModel,
				        FlaxBlenderbotForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				UpperCAmelCase__						:		Optional[int]              =  (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






				def _a (      self   )  ->	Optional[int]:
									__UpperCamelCase   							=FlaxBlenderbotModelTester(self   )






				def _a (      self   )  ->	Union[str, Any]:
									__UpperCamelCase							,				__UpperCamelCase   							=self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
														self.model_tester.check_use_cache_forward(UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_   )






				def _a (      self   )  ->	Optional[Any]:
									__UpperCamelCase							,				__UpperCamelCase   							=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(UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_   )






				def _a (      self   )  ->	Optional[int]:
									__UpperCamelCase							,				__UpperCamelCase   							=self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__   ):
																			__UpperCamelCase   							=self._prepare_for_class(UpperCamelCase_ ,       UpperCamelCase_   )
																			__UpperCamelCase   							=model_class(UpperCamelCase_   )

																			@jax.jit
																			def encode_jitted(A_ ,       A_=None ,       **A_   ):
																								return model.encode(input_ids=UpperCamelCase_ ,       attention_mask=UpperCamelCase_   )

																			with self.subTest('JIT Enabled'   ):
																								__UpperCamelCase   							=encode_jitted(**UpperCamelCase_   ).to_tuple()

																			with self.subTest('JIT Disabled'   ):
																								with jax.disable_jit():
																													__UpperCamelCase   							=encode_jitted(**UpperCamelCase_   ).to_tuple()

																			self.assertEqual(len(UpperCamelCase_   ) ,       len(UpperCamelCase_   )   )
																			for jitted_output, output in zip(UpperCamelCase_ ,       UpperCamelCase_   ):
																								self.assertEqual(jitted_output.shape ,       output.shape   )






				def _a (      self   )  ->	Dict:
									__UpperCamelCase							,				__UpperCamelCase   							=self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__   ):
																			__UpperCamelCase   							=model_class(UpperCamelCase_   )
																			__UpperCamelCase   							=model.encode(inputs_dict['input_ids'] ,       inputs_dict['attention_mask']   )

																			__UpperCamelCase   							={
																			    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																			    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																			    'encoder_outputs': encoder_outputs,
																			}

																			@jax.jit
																			def decode_jitted(A_ ,       A_ ,       A_   ):
																								return model.decode(
																								    decoder_input_ids=UpperCamelCase_ ,       decoder_attention_mask=UpperCamelCase_ ,       encoder_outputs=UpperCamelCase_ ,       )

																			with self.subTest('JIT Enabled'   ):
																								__UpperCamelCase   							=decode_jitted(**UpperCamelCase_   ).to_tuple()

																			with self.subTest('JIT Disabled'   ):
																								with jax.disable_jit():
																													__UpperCamelCase   							=decode_jitted(**UpperCamelCase_   ).to_tuple()

																			self.assertEqual(len(UpperCamelCase_   ) ,       len(UpperCamelCase_   )   )
																			for jitted_output, output in zip(UpperCamelCase_ ,       UpperCamelCase_   ):
																								self.assertEqual(jitted_output.shape ,       output.shape   )






				@slow
				def _a (      self   )  ->	List[Any]:







									for model_class_name in self.all_model_classes:
														__UpperCamelCase   							=model_class_name.from_pretrained('facebook/blenderbot-400M-distill'   )
														# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
														__UpperCamelCase   							=np.ones((1, 1)   ) * model.config.eos_token_id
														__UpperCamelCase   							=model(UpperCamelCase_   )
														self.assertIsNotNone(UpperCamelCase_   )






				@unittest.skipUnless(jax_device != 'cpu' ,       '3B test too slow on CPU.'   )
				@slow
				def _a (      self   )  ->	Optional[Any]:
									__UpperCamelCase   							={'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
									__UpperCamelCase   							={'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

									__UpperCamelCase   							=FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B' ,       from_pt=UpperCamelCase_   )
									__UpperCamelCase   							=BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'   )

									__UpperCamelCase   							=['Sam']
									__UpperCamelCase   							=tokenizer(UpperCamelCase_ ,       return_tensors='jax'   )

									__UpperCamelCase   							=model.generate(**UpperCamelCase_ ,       **UpperCamelCase_   )
									__UpperCamelCase   							='Sam is a great name. It means "sun" in Gaelic.'

									__UpperCamelCase   							=tokenizer.batch_decode(UpperCamelCase_ ,       **UpperCamelCase_   )
									assert generated_txt[0].strip() == tgt_text









import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

_A			=						'python tqdm regex requests packaging filelock numpy tokenizers'.split()
if sys.version_info < (3, 7):
   pkgs_to_check_at_runtime.append('dataclasses')
if sys.version_info < (3, 8):
   pkgs_to_check_at_runtime.append('importlib_metadata')

for pkg in pkgs_to_check_at_runtime:
   if pkg in deps:
      if pkg == "tokenizers":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_tokenizers_available

         if not is_tokenizers_available():
            continue  # not required, check version only if installed

      require_version_core(deps[pkg])
   else:
      raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : Optional[int]=None    ):
     require_version(deps[pkg]      ,					SCREAMING_SNAKE_CASE__    )





