

import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
			import PIL.Image

			from .features import FeatureType


a_      =     None
a_      =     '''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
a_      =     [
    np.dtype('|b1'),
    np.dtype('|u1'),
    np.dtype('<u2'),
    np.dtype('>u2'),
    np.dtype('<i2'),
    np.dtype('>i2'),
    np.dtype('<u4'),
    np.dtype('>u4'),
    np.dtype('<i4'),
    np.dtype('>i4'),
    np.dtype('<f4'),
    np.dtype('>f4'),
    np.dtype('<f8'),
    np.dtype('>f8'),
]






@dataclass
class _UpperCamelCase				:
			'''simple docstring'''







			lowerCamelCase__							     =True
			lowerCamelCase__							     =None
			# Automatically constructed
			lowerCamelCase__							     ="PIL.Image.Image"
			lowerCamelCase__							     =pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
			lowerCamelCase__							     =field(default='Image'				,						init=_lowercase				,						repr=_lowercase )
			def __call__(      self				:			Tuple						)				->							Union[str, Any]:

				"""simple docstring"""

				return self.pa_type
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]						)				->							Union[str, Any]:

				"""simple docstring"""

				if config.PIL_AVAILABLE:
					import PIL.Image
				else:
					raise ImportError("To support encoding images, please install 'Pillow'."						)

				if isinstance(__A   ,   __A						):
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							np.array(__A						)

				if isinstance(__A   ,   __A						):
					return {"path": value, "bytes": None}
				elif isinstance(__A   ,   __A						):
					return {"path": None, "bytes": value}
				elif isinstance(__A   ,   np.ndarray						):
					# convert the image array to PNG/TIFF bytes
					return encode_np_array(__A						)
				elif isinstance(__A   ,   PIL.Image.Image						):
					# convert the PIL image to bytes (default format is PNG/TIFF)
					return encode_pil_image(__A						)
				elif value.get("path"						) is not None and os.path.isfile(value["path"]						):
					# we set "bytes": None to not duplicate the data if they're already available locally
					return {"bytes": None, "path": value.get("path"						)}
				elif value.get("bytes"						) is not None or value.get("path"						) is not None:
					# store the image bytes, and path is used to infer the image format using the file extension
					return {"bytes": value.get("bytes"						), "path": value.get("path"						)}
				else:
					raise ValueError(
					    F"An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}."						)
			def 				__UpperCamelCase			(      self				:			Union[str, Any]   ,   a				:			dict   ,   a				:			Any=None						)				->							Tuple:

				"""simple docstring"""

				if not self.decode:
					raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead."						)

				if config.PIL_AVAILABLE:
					import PIL.Image
				else:
					raise ImportError("To support decoding images, please install 'Pillow'."						)

				if token_per_repo_id is None:
					SCREAMING_SNAKE_CASE :   int					  =							{}

				SCREAMING_SNAKE_CASE :   Dict					  =							value["""path"""], value["""bytes"""]
				if bytes_ is None:
					if path is None:
						raise ValueError(F"An image should have one of 'path' or 'bytes' but both are None in {value}."						)
					else:
						if is_local_path(__A						):
							SCREAMING_SNAKE_CASE :   Optional[Any]					  =							PIL.Image.open(__A						)
						else:
							SCREAMING_SNAKE_CASE :   Optional[int]					  =							path.split("::"						)[-1]
							try:
								SCREAMING_SNAKE_CASE :   Dict					  =							string_to_dict(__A   ,   config.HUB_DATASETS_URL						)["""repo_id"""]
								SCREAMING_SNAKE_CASE :   Dict					  =							token_per_repo_id.get(__A						)
							except ValueError:
								SCREAMING_SNAKE_CASE :   List[str]					  =							None
							with xopen(__A   ,   "rb"   ,   use_auth_token=__A						) as f:
								SCREAMING_SNAKE_CASE :   Optional[int]					  =							BytesIO(f.read()						)
							SCREAMING_SNAKE_CASE :   Any					  =							PIL.Image.open(bytes_						)
				else:
					SCREAMING_SNAKE_CASE :   Tuple					  =							PIL.Image.open(BytesIO(bytes_						)						)
				image.load()  # to avoid "Too many open files" errors
				return image
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[str]:

				"""simple docstring"""

				from .features import Value

				return (
				    self
				    if self.decode
				    else {
				        "bytes": Value("binary"						),
				        "path": Value("string"						),
				    }
				)
			def 				__UpperCamelCase			(      self				:			Dict   ,   a				:			Union[pa.StringArray, pa.StructArray, pa.ListArray]						)				->							Optional[int]:

				"""simple docstring"""

				if pa.types.is_string(storage.type						):
					SCREAMING_SNAKE_CASE :   List[Any]					  =							pa.array([None] * len(__A						)   ,   type=pa.binary()						)
					SCREAMING_SNAKE_CASE :   Tuple					  =							pa.StructArray.from_arrays([bytes_array, storage]   ,   ["bytes", "path"]   ,   mask=storage.is_null()						)
				elif pa.types.is_binary(storage.type						):
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							pa.array([None] * len(__A						)   ,   type=pa.string()						)
					SCREAMING_SNAKE_CASE :   Dict					  =							pa.StructArray.from_arrays([storage, path_array]   ,   ["bytes", "path"]   ,   mask=storage.is_null()						)
				elif pa.types.is_struct(storage.type						):
					if storage.type.get_field_index("bytes"						) >= 0:
						SCREAMING_SNAKE_CASE :   List[str]					  =							storage.field("bytes"						)
					else:
						SCREAMING_SNAKE_CASE :   List[str]					  =							pa.array([None] * len(__A						)   ,   type=pa.binary()						)
					if storage.type.get_field_index("path"						) >= 0:
						SCREAMING_SNAKE_CASE :   int					  =							storage.field("path"						)
					else:
						SCREAMING_SNAKE_CASE :   List[str]					  =							pa.array([None] * len(__A						)   ,   type=pa.string()						)
					SCREAMING_SNAKE_CASE :   List[Any]					  =							pa.StructArray.from_arrays([bytes_array, path_array]   ,   ["bytes", "path"]   ,   mask=storage.is_null()						)
				elif pa.types.is_list(storage.type						):
					SCREAMING_SNAKE_CASE :   Any					  =							pa.array(
					    [encode_np_array(np.array(__A						)						)["bytes"] if arr is not None else None for arr in storage.to_pylist()]   ,   type=pa.binary()   ,   )
					SCREAMING_SNAKE_CASE :   Dict					  =							pa.array([None] * len(__A						)   ,   type=pa.string()						)
					SCREAMING_SNAKE_CASE :   int					  =							pa.StructArray.from_arrays(
					    [bytes_array, path_array]   ,   ["bytes", "path"]   ,   mask=bytes_array.is_null()						)
				return array_cast(__A   ,   self.pa_type						)






			def 				__UpperCamelCase			(      self				:			List[str]   ,   a				:			pa.StructArray						)				->							List[Any]:

				"""simple docstring"""

				@no_op_if_value_is_null
				def path_to_bytes(a				:			List[str]						):
					with xopen(__A   ,   "rb"						) as f:
						SCREAMING_SNAKE_CASE :   Any					  =							f.read()
					return bytes_

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							pa.array(
				    [
				        (path_to_bytes(x["path"]						) if x["bytes"] is None else x["bytes"]) if x is not None else None
				        for x in storage.to_pylist()
				    ]   ,   type=pa.binary()   ,   )
				SCREAMING_SNAKE_CASE :   Any					  =							pa.array(
				    [os.path.basename(__A						) if path is not None else None for path in storage.field("path"						).to_pylist()]   ,   type=pa.string()   ,   )
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							pa.StructArray.from_arrays([bytes_array, path_array]   ,   ["bytes", "path"]   ,   mask=bytes_array.is_null()						)
				return array_cast(__A   ,   self.pa_type						)

def 			lowerCamelCase__			(       ):
	if config.PIL_AVAILABLE:
		import PIL.Image
	else:
		raise ImportError("To support encoding images, please install 'Pillow'.")

	global _IMAGE_COMPRESSION_FORMATS
	if _IMAGE_COMPRESSION_FORMATS is None:
		PIL.Image.init()
		SCREAMING_SNAKE_CASE :   Dict					  =							list(set(PIL.Image.OPEN.keys()) & set(PIL.Image.SAVE.keys()))
	return _IMAGE_COMPRESSION_FORMATS

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   Dict					  =							BytesIO()
	if image.format in list_image_compression_formats():
		SCREAMING_SNAKE_CASE :   int					  =							image.format
	else:
		SCREAMING_SNAKE_CASE :   Any					  =							"""PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
	image.save(a__				,      format=a__)
	return buffer.getvalue()

def 			lowerCamelCase__			(       _a):
	if hasattr(a__				,      "filename") and image.filename != "":
		return {"path": image.filename, "bytes": None}
	else:
		return {"path": None, "bytes": image_to_bytes(a__)}

def 			lowerCamelCase__			(       _a):
	if config.PIL_AVAILABLE:
		import PIL.Image
	else:
		raise ImportError("To support encoding images, please install 'Pillow'.")

	SCREAMING_SNAKE_CASE :   int					  =							array.dtype
	SCREAMING_SNAKE_CASE :   Dict					  =							dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							dtype.kind
	SCREAMING_SNAKE_CASE :   Any					  =							dtype.itemsize

	SCREAMING_SNAKE_CASE :   str					  =							None

	# Multi-channel array case (only np.dtype("|u1") is allowed)
	if array.shape[2:]:
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							np.dtype("|u1")
		if dtype_kind not in ["u", "i"]:
			raise TypeError(
			    f"Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.")
		if dtype is not dest_dtype:
			warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'")
    # Exact match
	elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
		SCREAMING_SNAKE_CASE :   List[Any]					  =							dtype
	else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
		while dtype_itemsize >= 1:
			SCREAMING_SNAKE_CASE :   Optional[Any]					  =							dtype_byteorder + dtype_kind + str(a__)
			SCREAMING_SNAKE_CASE :   Tuple					  =							np.dtype(a__)
			if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
				warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'")
				break
			else:
				dtype_itemsize //= 2

	if dest_dtype is None:
		raise TypeError(
		    f"Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}")
	SCREAMING_SNAKE_CASE :   Dict					  =							PIL.Image.fromarray(array.astype(a__))
	return {"path": None, "bytes": image_to_bytes(a__)}


def 			lowerCamelCase__			(       _a):
	if config.PIL_AVAILABLE:
		import PIL.Image
	else:
		raise ImportError("To support encoding images, please install 'Pillow'.")

	if objs:
		SCREAMING_SNAKE_CASE :   Any					  =							first_non_null_value(a__)
		if isinstance(a__				,      a__):
			return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
		if isinstance(a__				,      np.ndarray):
			SCREAMING_SNAKE_CASE :   Tuple					  =							no_op_if_value_is_null(a__)
			return [obj_to_image_dict_func(a__) for obj in objs]
		elif isinstance(a__				,      PIL.Image.Image):
			SCREAMING_SNAKE_CASE :   str					  =							no_op_if_value_is_null(a__)
			return [obj_to_image_dict_func(a__) for obj in objs]
		else:
			return objs
	else:
		return objs


import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_							:					List[str]		 =   logging.get_logger(__name__)

UpperCAmelCase_							:					str		 =   {
    '''microsoft/unispeech-large-1500h-cv''': (
        '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Dict     					=				'''unispeech'''




	def __init__(     self						:     str					,      __A						:     Tuple=32					,      __A						:     List[str]=768					,      __A						:     Dict=12					,      __A						:     Union[str, Any]=12					,      __A						:     Tuple=3072					,      __A						:     Any="gelu"					,      __A						:     int=0.1					,      __A						:     Optional[int]=0.1					,      __A						:     List[Any]=0.1					,      __A						:     Any=0.0					,      __A						:     List[str]=0.0					,      __A						:     int=0.1					,      __A						:     List[Any]=0.1					,      __A						:     List[str]=0.0_2					,      __A						:     List[str]=1e-5					,      __A						:     List[Any]="group"					,      __A						:     int="gelu"					,      __A						:     Any=(512, 512, 512, 512, 512, 512, 512)					,      __A						:     Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)					,      __A						:     Tuple=(10, 3, 3, 3, 3, 2, 2)					,      __A						:     Optional[int]=False					,      __A						:     Any=128					,      __A						:     Union[str, Any]=16					,      __A						:     Optional[Any]=False					,      __A						:     str=True					,      __A						:     Dict=0.0_5					,      __A						:     Optional[Any]=10					,      __A						:     Dict=2					,      __A						:     int=0.0					,      __A						:     List[str]=10					,      __A						:     str=0					,      __A						:     List[str]=320					,      __A						:     List[Any]=2					,      __A						:     Tuple=0.1					,      __A						:     Optional[int]=100					,      __A						:     Any=256					,      __A						:     Dict=256					,      __A						:     Tuple=0.1					,      __A						:     List[str]="mean"					,      __A						:     int=False					,      __A						:     List[str]=False					,      __A						:     List[Any]=256					,      __A						:     str=80					,      __A						:     Tuple=0					,      __A						:     Tuple=1					,      __A						:     int=2					,      __A						:     Dict=0.5					,      **__A						:     List[Any]					,      ):
		super().__init__(**__A					,      pad_token_id=__A					,      bos_token_id=__A					,      eos_token_id=__A							)
		__A :			Dict		    =			hidden_size
		__A :			Optional[Any]		    =			feat_extract_norm
		__A :			List[Any]		    =			feat_extract_activation
		__A :			str		    =			list(__A							)
		__A :			Optional[Any]		    =			list(__A							)
		__A :			Optional[int]		    =			list(__A							)
		__A :			List[Any]		    =			conv_bias
		__A :			Optional[int]		    =			num_conv_pos_embeddings
		__A :			List[Any]		    =			num_conv_pos_embedding_groups
		__A :			int		    =			len(self.conv_dim							)
		__A :			Optional[Any]		    =			num_hidden_layers
		__A :			List[str]		    =			intermediate_size
		__A :			Union[str, Any]		    =			hidden_act
		__A :			Optional[int]		    =			num_attention_heads
		__A :			Tuple		    =			hidden_dropout
		__A :			Optional[Any]		    =			attention_dropout
		__A :			Union[str, Any]		    =			activation_dropout
		__A :			Dict		    =			feat_proj_dropout
		__A :			Optional[int]		    =			final_dropout
		__A :			Dict		    =			layerdrop
		__A :			Optional[int]		    =			layer_norm_eps
		__A :			Optional[Any]		    =			initializer_range
		__A :			Optional[int]		    =			num_ctc_classes
		__A :			Dict		    =			vocab_size
		__A :			List[str]		    =			do_stable_layer_norm
		__A :			Tuple		    =			use_weighted_layer_sum
		__A :			Any		    =			classifier_proj_size

		if (
		    (len(self.conv_stride							) != self.num_feat_extract_layers)
		    or (len(self.conv_kernel							) != self.num_feat_extract_layers)
		    or (len(self.conv_dim							) != self.num_feat_extract_layers)
		):
			raise ValueError(
			    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
			    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
			    F""" {len(self.conv_dim							)}`, `len(config.conv_stride) = {len(self.conv_stride							)}`,"""
			    F""" `len(config.conv_kernel) = {len(self.conv_kernel							)}`."""							)

		# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
		__A :			Tuple		    =			apply_spec_augment
		__A :			Union[str, Any]		    =			mask_time_prob
		__A :			Optional[Any]		    =			mask_time_length
		__A :			List[Any]		    =			mask_time_min_masks
		__A :			List[Any]		    =			mask_feature_prob
		__A :			Any		    =			mask_feature_length
		__A :			List[Any]		    =			mask_feature_min_masks

		# parameters for pretraining with codevector quantized representations
		__A :			Any		    =			num_codevectors_per_group
		__A :			Tuple		    =			num_codevector_groups
		__A :			List[str]		    =			contrastive_logits_temperature
		__A :			Optional[int]		    =			feat_quantizer_dropout
		__A :			int		    =			num_negatives
		__A :			List[str]		    =			codevector_dim
		__A :			int		    =			proj_codevector_dim
		__A :			Union[str, Any]		    =			diversity_loss_weight

		# ctc loss
		__A :			List[str]		    =			ctc_loss_reduction
		__A :			Any		    =			ctc_zero_infinity

		# pretraining loss
		__A :			Union[str, Any]		    =			replace_prob




	@property
	def        lowerCAmelCase_       (     self						:     int							):
		return functools.reduce(operator.mul					,      self.conv_stride					,      1							)





