




import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY





@is_pipeline_test
@require_torch_or_tf
class      lowerCAmelCase						(					unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     int		=MODEL_FOR_CAUSAL_LM_MAPPING
     _SCREAMING_SNAKE_CASE     :     List[str]		=TF_MODEL_FOR_CAUSAL_LM_MAPPING



     @require_torch
     def 			a__  (					self       ):
            _A=	pipeline(task='text-generation'				,      model='sshleifer/tiny-ctrl'				,      framework='pt'       )
            # Using `do_sample=False` to force deterministic output
            _A=	text_generator('This is a test'				,      do_sample=lowerCAmelCase__       )
            self.assertEqual(
                lowerCAmelCase__				,      [
                    {
                        'generated_text': (
                            'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
                            ' oscope. FiliFili@@'
                        )
                    }
                ]				,      )

            _A=	text_generator(['This is a test', 'This is a second test']       )
            self.assertEqual(
                lowerCAmelCase__				,      [
                    [
                        {
                            'generated_text': (
                                'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
                                ' oscope. FiliFili@@'
                            )
                        }
                    ],
                    [
                        {
                            'generated_text': (
                                'This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'
                                ' oscope. oscope. FiliFili@@'
                            )
                        }
                    ],
                ]				,      )

            _A=	text_generator('This is a test'				,      do_sample=lowerCAmelCase__				,      num_return_sequences=2				,      return_tensors=lowerCAmelCase__       )
            self.assertEqual(
                lowerCAmelCase__				,      [
                    {'generated_token_ids': ANY(lowerCAmelCase__       )},
                    {'generated_token_ids': ANY(lowerCAmelCase__       )},
                ]				,      )
            _A=	text_generator.model.config.eos_token_id
            _A=	'<pad>'
            _A=	text_generator(
                ['This is a test', 'This is a second test']				,      do_sample=lowerCAmelCase__				,      num_return_sequences=2				,      batch_size=2				,      return_tensors=lowerCAmelCase__				,      )
            self.assertEqual(
                lowerCAmelCase__				,      [
                    [
                        {'generated_token_ids': ANY(lowerCAmelCase__       )},
                        {'generated_token_ids': ANY(lowerCAmelCase__       )},
                    ],
                    [
                        {'generated_token_ids': ANY(lowerCAmelCase__       )},
                        {'generated_token_ids': ANY(lowerCAmelCase__       )},
                    ],
                ]				,      )



     @require_tf
     def 			a__  (					self       ):
            _A=	pipeline(task='text-generation'				,      model='sshleifer/tiny-ctrl'				,      framework='tf'       )

            # Using `do_sample=False` to force deterministic output
            _A=	text_generator('This is a test'				,      do_sample=lowerCAmelCase__       )
            self.assertEqual(
                lowerCAmelCase__				,      [
                    {
                        'generated_text': (
                            'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
                            ' please,'
                        )
                    }
                ]				,      )

            _A=	text_generator(['This is a test', 'This is a second test']				,      do_sample=lowerCAmelCase__       )
            self.assertEqual(
                lowerCAmelCase__				,      [
                    [
                        {
                            'generated_text': (
                                'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
                                ' please,'
                            )
                        }
                    ],
                    [
                        {
                            'generated_text': (
                                'This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'
                                ' Cannes 閲閲Cannes Cannes Cannes 攵 please,'
                            )
                        }
                    ],
                ]				,      )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	TextGenerationPipeline(model=lowerCAmelCase__				,      tokenizer=lowerCAmelCase__       )
            return text_generator, ["This is a test", "Another test"]



     def 			a__  (					self       ):
            _A=	'Hello I believe in'
            _A=	pipeline('text-generation'				,      model='hf-internal-testing/tiny-random-gpt2'       )
            _A=	text_generator(lowerCAmelCase__       )
            self.assertEqual(
                lowerCAmelCase__				,      [{'generated_text': 'Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'}]				,      )

            _A=	text_generator(lowerCAmelCase__				,      stop_sequence=' fe'       )
            self.assertEqual(lowerCAmelCase__				,      [{'generated_text': 'Hello I believe in fe'}]       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	text_generator.model
            _A=	text_generator.tokenizer

            _A=	text_generator('This is a test'       )
            self.assertEqual(lowerCAmelCase__				,      [{'generated_text': ANY(lowerCAmelCase__       )}]       )
            self.assertTrue(outputs[0]['generated_text'].startswith('This is a test'       )       )

            _A=	text_generator('This is a test'				,      return_full_text=lowerCAmelCase__       )
            self.assertEqual(lowerCAmelCase__				,      [{'generated_text': ANY(lowerCAmelCase__       )}]       )
            self.assertNotIn('This is a test'				,      outputs[0]['generated_text']       )

            _A=	pipeline(task='text-generation'				,      model=lowerCAmelCase__				,      tokenizer=lowerCAmelCase__				,      return_full_text=lowerCAmelCase__       )
            _A=	text_generator('This is a test'       )
            self.assertEqual(lowerCAmelCase__				,      [{'generated_text': ANY(lowerCAmelCase__       )}]       )
            self.assertNotIn('This is a test'				,      outputs[0]['generated_text']       )

            _A=	text_generator('This is a test'				,      return_full_text=lowerCAmelCase__       )
            self.assertEqual(lowerCAmelCase__				,      [{'generated_text': ANY(lowerCAmelCase__       )}]       )
            self.assertTrue(outputs[0]['generated_text'].startswith('This is a test'       )       )

            _A=	text_generator(['This is great !', 'Something else']				,      num_return_sequences=2				,      do_sample=lowerCAmelCase__       )
            self.assertEqual(
                lowerCAmelCase__				,      [
                    [{'generated_text': ANY(lowerCAmelCase__       )}, {'generated_text': ANY(lowerCAmelCase__       )}],
                    [{'generated_text': ANY(lowerCAmelCase__       )}, {'generated_text': ANY(lowerCAmelCase__       )}],
                ]				,      )

            if text_generator.tokenizer.pad_token is not None:
                   _A=	text_generator(
                       ['This is great !', 'Something else']				,      num_return_sequences=2				,      batch_size=2				,      do_sample=lowerCAmelCase__       )
                   self.assertEqual(
                       lowerCAmelCase__				,      [
                           [{'generated_text': ANY(lowerCAmelCase__       )}, {'generated_text': ANY(lowerCAmelCase__       )}],
                           [{'generated_text': ANY(lowerCAmelCase__       )}, {'generated_text': ANY(lowerCAmelCase__       )}],
                       ]				,      )

            with self.assertRaises(lowerCAmelCase__       ):
                   _A=	text_generator('test'				,      return_full_text=lowerCAmelCase__				,      return_text=lowerCAmelCase__       )
            with self.assertRaises(lowerCAmelCase__       ):
                   _A=	text_generator('test'				,      return_full_text=lowerCAmelCase__				,      return_tensors=lowerCAmelCase__       )
            with self.assertRaises(lowerCAmelCase__       ):
                   _A=	text_generator('test'				,      return_text=lowerCAmelCase__				,      return_tensors=lowerCAmelCase__       )

            # Empty prompt is slighly special
            # it requires BOS token to exist.
            # Special case for Pegasus which will always append EOS so will
            # work even without BOS.
            if (
                text_generator.tokenizer.bos_token_id is not None
                or "Pegasus" in tokenizer.__class__.__name__
                or "Git" in model.__class__.__name__
            ):
                   _A=	text_generator(''       )
                   self.assertEqual(lowerCAmelCase__				,      [{'generated_text': ANY(lowerCAmelCase__       )}]       )
            else:
                   with self.assertRaises((ValueError, AssertionError)       ):
                          _A=	text_generator(''       )

            if text_generator.framework == "tf":
                   # TF generation does not support max_new_tokens, and it's impossible
                   # to control long generation with only max_length without
                   # fancy calculation, dismissing tests for now.
                   return
            # We don't care about infinite range models.
            # They already work.
            # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
            _A=	['RwkvForCausalLM', 'XGLMForCausalLM', 'GPTNeoXForCausalLM']
            if (
                tokenizer.model_max_length < 10000
                and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
            ):
                   # Handling of large generations
                   with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)       ):
                          text_generator('This is a test' * 500				,      max_new_tokens=20       )

                   _A=	text_generator('This is a test' * 500				,      handle_long_generation='hole'				,      max_new_tokens=20       )
                   # Hole strategy cannot work
                   with self.assertRaises(lowerCAmelCase__       ):
                          text_generator(
                              'This is a test' * 500				,      handle_long_generation='hole'				,      max_new_tokens=tokenizer.model_max_length + 10				,      )



     @require_torch
     @require_accelerate
     @require_torch_gpu
     def 			a__  (					self       ):
            import torch

            # Classic `model_kwargs`
            _A=	pipeline(
                model='hf-internal-testing/tiny-random-bloom'				,      model_kwargs={'device_map': 'auto', 'torch_dtype': torch.bfloataa}				,      )
            self.assertEqual(pipe.model.device				,      torch.device(0       )       )
            self.assertEqual(pipe.model.lm_head.weight.dtype				,      torch.bfloataa       )
            _A=	pipe('This is a test'       )
            self.assertEqual(
                lowerCAmelCase__				,      [
                    {
                        'generated_text': (
                            'This is a test test test test test test test test test test test test test test test test'
                            ' test'
                        )
                    }
                ]				,      )

            # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
            _A=	pipeline(model='hf-internal-testing/tiny-random-bloom'				,      device_map='auto'				,      torch_dtype=torch.bfloataa       )
            self.assertEqual(pipe.model.device				,      torch.device(0       )       )
            self.assertEqual(pipe.model.lm_head.weight.dtype				,      torch.bfloataa       )
            _A=	pipe('This is a test'       )
            self.assertEqual(
                lowerCAmelCase__				,      [
                    {
                        'generated_text': (
                            'This is a test test test test test test test test test test test test test test test test'
                            ' test'
                        )
                    }
                ]				,      )

            # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
            _A=	pipeline(model='hf-internal-testing/tiny-random-bloom'				,      device_map='auto'       )
            self.assertEqual(pipe.model.device				,      torch.device(0       )       )
            self.assertEqual(pipe.model.lm_head.weight.dtype				,      torch.floataa       )
            _A=	pipe('This is a test'       )
            self.assertEqual(
                lowerCAmelCase__				,      [
                    {
                        'generated_text': (
                            'This is a test test test test test test test test test test test test test test test test'
                            ' test'
                        )
                    }
                ]				,      )



     @require_torch
     @require_torch_gpu
     def 			a__  (					self       ):
            import torch

            _A=	pipeline(model='hf-internal-testing/tiny-random-bloom'				,      device=0				,      torch_dtype=torch.floataa       )
            pipe('This is a test'       )



     @require_torch
     @require_accelerate
     @require_torch_gpu
     def 			a__  (					self       ):
            import torch

            _A=	pipeline(model='hf-internal-testing/tiny-random-bloom'				,      device_map='auto'				,      torch_dtype=torch.floataa       )
            pipe('This is a test'				,      do_sample=lowerCAmelCase__				,      top_p=0.5       )





     def 			a__  (					self       ):
            _A=	'Hello world'
            _A=	pipeline('text-generation'				,      model='hf-internal-testing/tiny-random-gpt2'       )
            if text_generator.model.framework == "tf":
                   _A=	logging.get_logger('transformers.generation.tf_utils'       )
            else:
                   _A=	logging.get_logger('transformers.generation.utils'       )
            _A=	'Both `max_new_tokens`'  # The beggining of the message to be checked in this test

            # Both are set by the user -> log warning
            with CaptureLogger(lowerCAmelCase__       ) as cl:
                   _A=	text_generator(lowerCAmelCase__				,      max_length=10				,      max_new_tokens=1       )
            self.assertIn(lowerCAmelCase__				,      cl.out       )

            # The user only sets one -> no warning
            with CaptureLogger(lowerCAmelCase__       ) as cl:
                   _A=	text_generator(lowerCAmelCase__				,      max_new_tokens=1       )
            self.assertNotIn(lowerCAmelCase__				,      cl.out       )

            with CaptureLogger(lowerCAmelCase__       ) as cl:
                   _A=	text_generator(lowerCAmelCase__				,      max_length=10       )
            self.assertNotIn(lowerCAmelCase__				,      cl.out       )
from jiwer import compute_measures

import datasets


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

UpperCAmelCase_								=							'''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The 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.

This 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.

Word error rate can then be computed as:

WER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''

UpperCAmelCase_								=							'''
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> wer = datasets.load_metric("wer")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,		_KWARGS_DESCRIPTION			)
class      lowerCAmelCase						(					datasets.Metric			):



     def 			a__  (					self       ):
            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 			a__  (					self				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      lowerCAmelCase__=False       ):
            if concatenate_texts:
                   return compute_measures(lowerCAmelCase__				,      lowerCAmelCase__       )["wer"]
            else:
                   _A=	0
                   _A=	0
                   for prediction, reference in zip(lowerCAmelCase__				,      lowerCAmelCase__       ):
                          _A=	compute_measures(lowerCAmelCase__				,      lowerCAmelCase__       )
                          incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                          total += measures["substitutions"] + measures["deletions"] + measures["hits"]
                   return incorrect / total