



"""simple docstring"""


import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np




def   snake_case		(     UpperCamelCase__       :						bytes			,				UpperCamelCase__       :						int    )   ->      np.array:
      lowerCamelCase					:					List[str]        =	F'{sampling_rate}'
      lowerCamelCase					:					Optional[Any]        =	"""1"""
      lowerCamelCase					:					str        =	"""f32le"""
      lowerCamelCase					:					Optional[int]        =	[
          """ffmpeg""",
          """-i""",
          """pipe:0""",
          """-ac""",
          ac,
          """-ar""",
          ar,
          """-f""",
          format_for_conversion,
          """-hide_banner""",
          """-loglevel""",
          """quiet""",
          """pipe:1""",
      ]

      try:
            with subprocess.Popen(UpperCamelCase__			,				stdin=subprocess.PIPE			,				stdout=subprocess.PIPE    ) as ffmpeg_process:
                  lowerCamelCase					:					Dict        =	ffmpeg_process.communicate(UpperCamelCase__    )
      except FileNotFoundError as error:
            raise ValueError("""ffmpeg was not found but is required to load audio files from filename"""    ) from error
      lowerCamelCase					:					Union[str, Any]        =	output_stream[0]
      lowerCamelCase					:					List[str]        =	np.frombuffer(UpperCamelCase__			,				np.floataa    )
      if audio.shape[0] == 0:
            raise ValueError("""Malformed soundfile"""    )
      return audio




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						float			,				UpperCamelCase__       :						str = "f32le"			,				)   ->      Dict:
      lowerCamelCase					:					Any        =	F'{sampling_rate}'
      lowerCamelCase					:					Tuple        =	"""1"""
      if format_for_conversion == "s16le":
            lowerCamelCase					:					Optional[Any]        =	2
      elif format_for_conversion == "f32le":
            lowerCamelCase					:					Tuple        =	4
      else:
            raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'    )

      lowerCamelCase					:					Optional[Any]        =	platform.system()
      if system == "Linux":
            lowerCamelCase					:					Optional[int]        =	"""alsa"""
            lowerCamelCase					:					Dict        =	"""default"""
      elif system == "Darwin":
            lowerCamelCase					:					Any        =	"""avfoundation"""
            lowerCamelCase					:					str        =	""":0"""
      elif system == "Windows":
            lowerCamelCase					:					int        =	"""dshow"""
            lowerCamelCase					:					Dict        =	"""default"""

      lowerCamelCase					:					Union[str, Any]        =	[
          """ffmpeg""",
          """-f""",
          format_,
          """-i""",
          input_,
          """-ac""",
          ac,
          """-ar""",
          ar,
          """-f""",
          format_for_conversion,
          """-fflags""",
          """nobuffer""",
          """-hide_banner""",
          """-loglevel""",
          """quiet""",
          """pipe:1""",
      ]
      lowerCamelCase					:					List[str]        =	int(round(sampling_rate * chunk_length_s    )    ) * size_of_sample
      lowerCamelCase					:					Union[str, Any]        =	_ffmpeg_stream(UpperCamelCase__			,				UpperCamelCase__    )
      for item in iterator:
            yield item




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						float			,				UpperCamelCase__       :						Optional[int] = None			,				UpperCamelCase__       :						Optional[Union[Tuple[float, float], float]] = None			,				UpperCamelCase__       :						str = "f32le"			,				)   ->      Any:
      if stream_chunk_s is not None:
            lowerCamelCase					:					str        =	stream_chunk_s
      else:
            lowerCamelCase					:					Optional[int]        =	chunk_length_s

      lowerCamelCase					:					Union[str, Any]        =	ffmpeg_microphone(UpperCamelCase__			,				UpperCamelCase__			,				format_for_conversion=UpperCamelCase__    )
      if format_for_conversion == "s16le":
            lowerCamelCase					:					int        =	np.intaa
            lowerCamelCase					:					Dict        =	2
      elif format_for_conversion == "f32le":
            lowerCamelCase					:					Optional[int]        =	np.floataa
            lowerCamelCase					:					Any        =	4
      else:
            raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'    )

      if stride_length_s is None:
            lowerCamelCase					:					Any        =	chunk_length_s / 6
      lowerCamelCase					:					Optional[Any]        =	int(round(sampling_rate * chunk_length_s    )    ) * size_of_sample
      if isinstance(UpperCamelCase__			,				(int, float)    ):
            lowerCamelCase					:					List[Any]        =	[stride_length_s, stride_length_s]

      lowerCamelCase					:					str        =	int(round(sampling_rate * stride_length_s[0]    )    ) * size_of_sample
      lowerCamelCase					:					Dict        =	int(round(sampling_rate * stride_length_s[1]    )    ) * size_of_sample
      lowerCamelCase					:					str        =	datetime.datetime.now()
      lowerCamelCase					:					Any        =	datetime.timedelta(seconds=UpperCamelCase__    )
      for item in chunk_bytes_iter(UpperCamelCase__			,				UpperCamelCase__			,				stride=(stride_left, stride_right)			,				stream=UpperCamelCase__    ):
            # Put everything back in numpy scale
            lowerCamelCase					:					Tuple        =	np.frombuffer(item["""raw"""]			,				dtype=UpperCamelCase__    )
            lowerCamelCase					:					List[str]        =	(
                item["""stride"""][0] // size_of_sample,
                item["""stride"""][1] // size_of_sample,
            )
            lowerCamelCase					:					List[Any]        =	sampling_rate
            audio_time += delta
            if datetime.datetime.now() > audio_time + 10 * delta:
                  # We're late !! SKIP
                  continue
            yield item




def   snake_case		(     UpperCamelCase__       :						List[Any]			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						Tuple[int, int]			,				UpperCamelCase__       :						bool = False    )   ->      Optional[Any]:
      lowerCamelCase					:					Dict        =	B""""""
      lowerCamelCase					:					Optional[int]        =	stride
      if stride_left + stride_right >= chunk_len:
            raise ValueError(
                F'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'    )
      lowerCamelCase					:					Dict        =	0
      for raw in iterator:
            acc += raw
            if stream and len(UpperCamelCase__    ) < chunk_len:
                  lowerCamelCase					:					Union[str, Any]        =	(_stride_left, 0)
                  yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
            else:
                  while len(UpperCamelCase__    ) >= chunk_len:
                        # We are flushing the accumulator
                        lowerCamelCase					:					List[str]        =	(_stride_left, stride_right)
                        lowerCamelCase					:					int        =	{"""raw""": acc[:chunk_len], """stride""": stride}
                        if stream:
                              lowerCamelCase					:					str        =	False
                        yield item
                        lowerCamelCase					:					Union[str, Any]        =	stride_left
                        lowerCamelCase					:					Dict        =	acc[chunk_len - stride_left - stride_right :]
    # Last chunk
      if len(UpperCamelCase__    ) > stride_left:
            lowerCamelCase					:					Dict        =	{"""raw""": acc, """stride""": (_stride_left, 0)}
            if stream:
                  lowerCamelCase					:					List[str]        =	False
            yield item






def   snake_case		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						int    )   ->      List[Any]:
      lowerCamelCase					:					Optional[int]        =	2**24  # 16Mo
      try:
            with subprocess.Popen(UpperCamelCase__			,				stdout=subprocess.PIPE			,				bufsize=UpperCamelCase__    ) as ffmpeg_process:
                  while True:
                        lowerCamelCase					:					List[str]        =	ffmpeg_process.stdout.read(UpperCamelCase__    )
                        if raw == b"":
                              break
                        yield raw
      except FileNotFoundError as error:
            raise ValueError("""ffmpeg was not found but is required to stream audio files from filename"""    ) from error








"""simple docstring"""


import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
    from transformers.models.esm.modeling_esm import (
        ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
        EsmEmbeddings,
        create_position_ids_from_input_ids,
    )


class 			A__	:


     """simple docstring"""

     def __init__(	self:						List[str]     ,		__a:						List[str]     ,		__a:						Dict=13     ,		__a:						Tuple=7     ,		__a:						Dict=False     ,		__a:						str=True     ,		__a:						List[Any]=False     ,		__a:						Dict=True     ,		__a:						Any=33     ,		__a:						Optional[Any]=32     ,		__a:						List[Any]=5     ,		__a:						Any=4     ,		__a:						Dict=37     ,		__a:						str="gelu"     ,		__a:						str=0.1     ,		__a:						int=0.1     ,		__a:						Optional[int]=512     ,		__a:						List[Any]=16     ,		__a:						int=2     ,		__a:						int=0.02     ,		__a:						Optional[int]=3     ,		__a:						str=4     ,		__a:						Tuple=None     ,		)->    Tuple:
           lowerCamelCase					:					Union[str, Any]        =	parent
           lowerCamelCase					:					Tuple        =	batch_size
           lowerCamelCase					:					Any        =	seq_length
           lowerCamelCase					:					Any        =	is_training
           lowerCamelCase					:					Tuple        =	use_input_mask
           lowerCamelCase					:					int        =	use_token_type_ids
           lowerCamelCase					:					List[str]        =	use_labels
           lowerCamelCase					:					Optional[int]        =	vocab_size
           lowerCamelCase					:					Tuple        =	hidden_size
           lowerCamelCase					:					List[str]        =	num_hidden_layers
           lowerCamelCase					:					Optional[int]        =	num_attention_heads
           lowerCamelCase					:					Optional[Any]        =	intermediate_size
           lowerCamelCase					:					Optional[Any]        =	hidden_act
           lowerCamelCase					:					Union[str, Any]        =	hidden_dropout_prob
           lowerCamelCase					:					Optional[Any]        =	attention_probs_dropout_prob
           lowerCamelCase					:					Any        =	max_position_embeddings
           lowerCamelCase					:					str        =	type_vocab_size
           lowerCamelCase					:					List[Any]        =	type_sequence_label_size
           lowerCamelCase					:					Optional[Any]        =	initializer_range
           lowerCamelCase					:					Union[str, Any]        =	num_labels
           lowerCamelCase					:					Optional[Any]        =	num_choices
           lowerCamelCase					:					Any        =	scope

     def 		a__						(	self:						Optional[int]	)->    List[Any]:
           lowerCamelCase					:					List[Any]        =	ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size	)

           lowerCamelCase					:					Dict        =	None
           if self.use_input_mask:
                 lowerCamelCase					:					Tuple        =	random_attention_mask([self.batch_size, self.seq_length]	)

           lowerCamelCase					:					Any        =	None
           lowerCamelCase					:					int        =	None
           lowerCamelCase					:					Union[str, Any]        =	None
           if self.use_labels:
                 lowerCamelCase					:					Optional[Any]        =	ids_tensor([self.batch_size]     ,		self.type_sequence_label_size	)
                 lowerCamelCase					:					Optional[int]        =	ids_tensor([self.batch_size, self.seq_length]     ,		self.num_labels	)
                 lowerCamelCase					:					Union[str, Any]        =	ids_tensor([self.batch_size]     ,		self.num_choices	)

           lowerCamelCase					:					List[str]        =	self.get_config()

           return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

     def 		a__						(	self:						Tuple	)->    Union[str, Any]:
           return EsmConfig(
               vocab_size=self.vocab_size     ,		hidden_size=self.hidden_size     ,		pad_token_id=1     ,		num_hidden_layers=self.num_hidden_layers     ,		num_attention_heads=self.num_attention_heads     ,		intermediate_size=self.intermediate_size     ,		hidden_act=self.hidden_act     ,		hidden_dropout_prob=self.hidden_dropout_prob     ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,		max_position_embeddings=self.max_position_embeddings     ,		type_vocab_size=self.type_vocab_size     ,		initializer_range=self.initializer_range     ,		)

     def 		a__						(	self:						List[Any]     ,		__a:						List[str]     ,		__a:						str     ,		__a:						Tuple     ,		__a:						List[str]     ,		__a:						List[str]     ,		__a:						str	)->    int:
           lowerCamelCase					:					Optional[int]        =	EsmModel(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					int        =	model(__a     ,		attention_mask=__a	)
           lowerCamelCase					:					str        =	model(__a	)
           lowerCamelCase					:					Optional[Any]        =	model(__a	)

           self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size)	)
           self.parent.assertEqual(result.pooler_output.shape     ,		(self.batch_size, self.hidden_size)	)

     def 		a__						(	self:						int     ,		__a:						Union[str, Any]     ,		__a:						Optional[int]     ,		__a:						List[str]     ,		__a:						str     ,		__a:						List[str]     ,		__a:						Tuple	)->    int:
           lowerCamelCase					:					str        =	EsmForMaskedLM(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					List[Any]        =	model(__a     ,		attention_mask=__a     ,		labels=__a	)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.vocab_size)	)

     def 		a__						(	self:						List[str]     ,		__a:						List[Any]     ,		__a:						List[str]     ,		__a:						int     ,		__a:						Union[str, Any]     ,		__a:						List[Any]     ,		__a:						Tuple	)->    List[str]:
           lowerCamelCase					:					Tuple        =	self.num_labels
           lowerCamelCase					:					Dict        =	EsmForTokenClassification(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					int        =	model(__a     ,		attention_mask=__a     ,		labels=__a	)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.num_labels)	)







     def 		a__						(	self:						Optional[int]	)->    Optional[int]:
           lowerCamelCase					:					Any        =	self.prepare_config_and_inputs()
           (
               (
               lowerCamelCase
           )     ,			(
               lowerCamelCase
           )     ,			(
               lowerCamelCase
           )     ,			(
               lowerCamelCase
           )     ,			(
               lowerCamelCase
           )     ,			(
               lowerCamelCase
           )     ,			
           )					:					Tuple        =	config_and_inputs
           lowerCamelCase					:					List[Any]        =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
           return config, inputs_dict


@require_torch
class 			A__	(						__lowercase ,     __lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					Any													=False

     snake_case__	:					Dict													=(
         (
             EsmForMaskedLM,
             EsmModel,
             EsmForSequenceClassification,
             EsmForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     snake_case__	:					Dict													=()
     snake_case__	:					Optional[int]													=(
         {
             '''feature-extraction''': EsmModel,
             '''fill-mask''': EsmForMaskedLM,
             '''text-classification''': EsmForSequenceClassification,
             '''token-classification''': EsmForTokenClassification,
             '''zero-shot''': EsmForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     snake_case__	:					Any													=True

     def 		a__						(	self:						Optional[int]	)->    Optional[int]:
           lowerCamelCase					:					Optional[Any]        =	EsmModelTester(self	)
           lowerCamelCase					:					Any        =	ConfigTester(self     ,		config_class=__a     ,		hidden_size=37	)

     def 		a__						(	self:						List[Any]	)->    Optional[Any]:
           self.config_tester.run_common_tests()

     def 		a__						(	self:						int	)->    Optional[Any]:
           lowerCamelCase					:					Union[str, Any]        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__a	)

     def 		a__						(	self:						Tuple	)->    Any:
           lowerCamelCase					:					List[str]        =	self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                 lowerCamelCase					:					Tuple        =	type
                 self.model_tester.create_and_check_model(*__a	)

     def 		a__						(	self:						List[str]	)->    Tuple:
           lowerCamelCase					:					Optional[int]        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*__a	)

     def 		a__						(	self:						int	)->    Optional[Any]:
           lowerCamelCase					:					List[Any]        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*__a	)

     @slow
     def 		a__						(	self:						Any	)->    List[Any]:
           for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase					:					int        =	EsmModel.from_pretrained(__a	)
                 self.assertIsNotNone(__a	)

     def 		a__						(	self:						str	)->    List[str]:
           lowerCamelCase					:					Union[str, Any]        =	self.model_tester.prepare_config_and_inputs()[0]
           lowerCamelCase					:					Union[str, Any]        =	EsmEmbeddings(config=__a	)

           lowerCamelCase					:					List[str]        =	torch.as_tensor([[12, 31, 13, model.padding_idx]]	)
           lowerCamelCase					:					Union[str, Any]        =	torch.as_tensor(
               [
                   [
                       0 + model.padding_idx + 1,
                       1 + model.padding_idx + 1,
                       2 + model.padding_idx + 1,
                       model.padding_idx,
                   ]
               ]	)
           lowerCamelCase					:					Optional[Any]        =	create_position_ids_from_input_ids(__a     ,		model.padding_idx	)
           self.assertEqual(position_ids.shape     ,		expected_positions.shape	)
           self.assertTrue(torch.all(torch.eq(__a     ,		__a	)	)	)

     def 		a__						(	self:						Optional[int]	)->    int:
           lowerCamelCase					:					List[str]        =	self.model_tester.prepare_config_and_inputs()[0]
           lowerCamelCase					:					Any        =	EsmEmbeddings(config=__a	)

           lowerCamelCase					:					Dict        =	torch.empty(2     ,		4     ,		30	)
           lowerCamelCase					:					List[Any]        =	[
               0 + embeddings.padding_idx + 1,
               1 + embeddings.padding_idx + 1,
               2 + embeddings.padding_idx + 1,
               3 + embeddings.padding_idx + 1,
           ]
           lowerCamelCase					:					Any        =	torch.as_tensor([expected_single_positions, expected_single_positions]	)
           lowerCamelCase					:					List[str]        =	embeddings.create_position_ids_from_inputs_embeds(__a	)
           self.assertEqual(position_ids.shape     ,		expected_positions.shape	)
           self.assertTrue(torch.all(torch.eq(__a     ,		__a	)	)	)

     @unittest.skip("""Esm does not support embedding resizing"""	)
     def 		a__						(	self:						Any	)->    Optional[Any]:
           pass

     @unittest.skip("""Esm does not support embedding resizing"""	)
     def 		a__						(	self:						Dict	)->    Dict:
           pass







     @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""	)
     def 		a__						(	self:						List[str]	)->    Dict:
           pass


@require_torch
class 			A__	(						__lowercase):


     """simple docstring"""

     @slow
     def 		a__						(	self:						Any	)->    Union[str, Any]:
           with torch.no_grad():
                 lowerCamelCase					:					Union[str, Any]        =	EsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D"""	)
                 model.eval()
                 lowerCamelCase					:					List[str]        =	torch.tensor([[0, 1, 2, 3, 4, 5]]	)
                 lowerCamelCase					:					Tuple        =	model(__a	)[0]

                 lowerCamelCase					:					Dict        =	33

                 lowerCamelCase					:					List[str]        =	torch.Size((1, 6, vocab_size)	)
                 self.assertEqual(output.shape     ,		__a	)

                 lowerCamelCase					:					Tuple        =	torch.tensor(
                     [[[8.92_15, -10.58_98, -6.46_71], [-6.39_67, -13.91_14, -1.12_12], [-7.78_12, -13.95_16, -3.74_06]]]	)
                 self.assertTrue(torch.allclose(output[:, :3, :3]     ,		__a     ,		atol=1e-4	)	)







     @slow
     def 		a__						(	self:						Dict	)->    str:
           with torch.no_grad():
                 lowerCamelCase					:					Any        =	EsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D"""	)
                 model.eval()

                 lowerCamelCase					:					Optional[Any]        =	torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]	)
                 lowerCamelCase					:					Any        =	model(__a	)[0]
                 # compare the actual values for a slice.
                 lowerCamelCase					:					Tuple        =	torch.tensor(
                     [[[0.14_44, 0.54_13, 0.32_48], [0.30_34, 0.00_53, 0.31_08], [0.32_28, -0.24_99, 0.34_15]]]	)
                 self.assertTrue(torch.allclose(output[:, :3, :3]     ,		__a     ,		atol=1e-4	)	)




