






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__      :     List[Any]       =					'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     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_=None						,  lowerCamelCase_=None						,  lowerCamelCase_=None						,  lowerCamelCase_=None						,  lowerCamelCase_=None						,  lowerCamelCase_=None						,  ):






				'''simple docstring'''
				if attention_mask is None:
								lowercase__  					=     np.where(input_ids != config.pad_token_id						,  1						,  0							)
				if decoder_attention_mask is None:
								lowercase__  					=     np.where(decoder_input_ids != config.pad_token_id						,  1						,  0							)
				if head_mask is None:
								lowercase__  					=     np.ones((config.encoder_layers, config.encoder_attention_heads)							)
				if decoder_head_mask is None:
								lowercase__  					=     np.ones((config.decoder_layers, config.decoder_attention_heads)							)
				if cross_attn_head_mask is None:
								lowercase__  					=     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		:       Optional[Any],    lowerCamelCase		:       str,    lowerCamelCase		:       List[str]=13,    lowerCamelCase		:       str=7,    lowerCamelCase		:       Dict=True,    lowerCamelCase		:       Dict=False,    lowerCamelCase		:       List[Any]=99,    lowerCamelCase		:       Any=16,    lowerCamelCase		:       Optional[int]=2,    lowerCamelCase		:       str=4,    lowerCamelCase		:       Union[str, Any]=4,    lowerCamelCase		:       str="gelu",    lowerCamelCase		:       List[str]=0.1,    lowerCamelCase		:       List[Any]=0.1,    lowerCamelCase		:       List[Any]=32,    lowerCamelCase		:       str=2,    lowerCamelCase		:       Dict=1,    lowerCamelCase		:       Tuple=0,    lowerCamelCase		:       List[str]=0.02,    ):

								'''simple docstring'''
								lowercase__  					=     parent
								lowercase__  					=     batch_size
								lowercase__  					=     seq_length
								lowercase__  					=     is_training
								lowercase__  					=     use_labels
								lowercase__  					=     vocab_size
								lowercase__  					=     hidden_size
								lowercase__  					=     num_hidden_layers
								lowercase__  					=     num_attention_heads
								lowercase__  					=     intermediate_size
								lowercase__  					=     hidden_act
								lowercase__  					=     hidden_dropout_prob
								lowercase__  					=     attention_probs_dropout_prob
								lowercase__  					=     max_position_embeddings
								lowercase__  					=     eos_token_id
								lowercase__  					=     pad_token_id
								lowercase__  					=     bos_token_id
								lowercase__  					=     initializer_range




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								lowercase__  					=     np.clip(ids_tensor([self.batch_size, self.seq_length - 1],    self.vocab_size    ),    3,    self.vocab_size    )
								lowercase__  					=     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),    dtype=np.intaa    )),    -1    )

								lowercase__  					=     shift_tokens_right(lowerCamelCase,    1,    2    )

								lowercase__  					=     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=lowerCamelCase,    )
								lowercase__  					=     prepare_blenderbot_inputs_dict(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    )
								return config, inputs_dict




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								lowercase__					,		lowercase__  					=     self.prepare_config_and_inputs()
								return config, inputs_dict




				def 							lowercase__  (	self		:       str,    lowerCamelCase		:       Union[str, Any],    lowerCamelCase		:       Optional[Any],    lowerCamelCase		:       Union[str, Any]    ):

								'''simple docstring'''
								lowercase__  					=     20
								lowercase__  					=     model_class_name(lowerCamelCase    )

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

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

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

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

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

								lowercase__  					=     model.decode(lowerCamelCase,    lowerCamelCase    )

								lowercase__  					=     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 							lowercase__  (	self		:       Optional[int],    lowerCamelCase		:       Dict,    lowerCamelCase		:       Optional[Any],    lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     20
								lowercase__  					=     model_class_name(lowerCamelCase    )

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

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

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

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

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

								lowercase__  					=     model.decode(lowerCamelCase,    lowerCamelCase,    decoder_attention_mask=lowerCamelCase    )

								lowercase__  					=     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"""




				lowercase__											=			99




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								lowercase__  					=     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,    )

								lowercase__  					=     input_ids.shape[0]
								lowercase__  					=     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 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								lowercase__					,		lowercase__					,		lowercase__  					=     self._get_config_and_data()
								lowercase__  					=     FlaxBlenderbotForConditionalGeneration(lowerCamelCase    )
								lowercase__  					=     lm_model(input_ids=lowerCamelCase    )
								lowercase__  					=     (batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape,    lowerCamelCase    )




				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								lowercase__  					=     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,    )
								lowercase__  					=     FlaxBlenderbotForConditionalGeneration(lowerCamelCase    )
								lowercase__  					=     np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],    dtype=np.intaa    )
								lowercase__  					=     np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],    dtype=np.intaa    )
								lowercase__  					=     lm_model(input_ids=lowerCamelCase,    decoder_input_ids=lowerCamelCase    )
								lowercase__  					=     (*summary.shape, config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape,    lowerCamelCase    )




				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								lowercase__  					=     np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],    dtype=np.intaa    )
								lowercase__  					=     shift_tokens_right(lowerCamelCase,    1,    2    )
								lowercase__  					=     np.equal(lowerCamelCase,    1    ).astype(np.floataa    ).sum()
								lowercase__  					=     np.equal(lowerCamelCase,    1    ).astype(np.floataa    ).sum()
								self.assertEqual(shifted.shape,    input_ids.shape    )
								self.assertEqual(lowerCamelCase,    n_pad_before - 1    )
								self.assertTrue(np.equal(shifted[:, 0],    2    ).all()    )



@require_flax
class  _UpperCAmelCase   (			A__						,unittest.TestCase						,A__      ):

				"""simple docstring"""




				lowercase__											=			True
				lowercase__											=			(
				    (
				        FlaxBlenderbotModel,
				        FlaxBlenderbotForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				lowercase__											=			(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								lowercase__  					=     FlaxBlenderbotModelTester(self    )




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								lowercase__					,		lowercase__  					=     self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    )




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								lowercase__					,		lowercase__  					=     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(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    )




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								lowercase__					,		lowercase__  					=     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__    ):
																lowercase__  					=     self._prepare_for_class(lowerCamelCase,    lowerCamelCase    )
																lowercase__  					=     model_class(lowerCamelCase    )

																@jax.jit
																def encode_jitted(lowerCamelCase		:       Any,    lowerCamelCase		:       Any=None,    **lowerCamelCase		:       Dict    ):
																				return model.encode(input_ids=lowerCamelCase,    attention_mask=lowerCamelCase    )

																with self.subTest('''JIT Enabled'''    ):
																				lowercase__  					=     encode_jitted(**lowerCamelCase    ).to_tuple()

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

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




				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								lowercase__					,		lowercase__  					=     self.model_tester.prepare_config_and_inputs_for_common()

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

																lowercase__  					=     {
																    '''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(lowerCamelCase		:       Any,    lowerCamelCase		:       Dict,    lowerCamelCase		:       Any    ):
																				return model.decode(
																				    decoder_input_ids=lowerCamelCase,    decoder_attention_mask=lowerCamelCase,    encoder_outputs=lowerCamelCase,    )

																with self.subTest('''JIT Enabled'''    ):
																				lowercase__  					=     decode_jitted(**lowerCamelCase    ).to_tuple()

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

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




				@slow
				def 							lowercase__  (	self		:       List[str]    ):

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




				@unittest.skipUnless(jax_device != '''cpu''',    '''3B test too slow on CPU.'''    )
				@slow
				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								lowercase__  					=     {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
								lowercase__  					=     {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

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

								lowercase__  					=     ['''Sam''']
								lowercase__  					=     tokenizer(lowerCamelCase,    return_tensors='''jax'''    )

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

								lowercase__  					=     tokenizer.batch_decode(lowerCamelCase,    **lowerCamelCase    )
								assert generated_txt[0].strip() == tgt_text












from jiwer import compute_measures

import datasets


A__      :     Tuple       =					'\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

A__      :     Optional[int]       =					'\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

A__      :     Dict       =					'\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,_KWARGS_DESCRIPTION      )
class  _UpperCAmelCase   (			datasets.Metric      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								return datasets.MetricInfo(
								    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
								        {
								            '''predictions''': datasets.Value('''string''',    id='''sequence'''    ),
								            '''references''': datasets.Value('''string''',    id='''sequence'''    ),
								        }    ),    codebase_urls=['''https://github.com/jitsi/jiwer/'''],    reference_urls=[
								        '''https://en.wikipedia.org/wiki/Word_error_rate''',
								    ],    )




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       str=None,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       List[str]=False    ):

								'''simple docstring'''
								if concatenate_texts:
												return compute_measures(lowerCamelCase,    lowerCamelCase    )["wer"]
								else:
												lowercase__  					=     0
												lowercase__  					=     0
												for prediction, reference in zip(lowerCamelCase,    lowerCamelCase    ):
																lowercase__  					=     compute_measures(lowerCamelCase,    lowerCamelCase    )
																incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																total += measures["substitutions"] + measures["deletions"] + measures["hits"]
												return incorrect / total




