

from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
		from ..models.auto import AutoTokenizer


class SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				raise NotImplementedError()







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				raise NotImplementedError()







class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase = False			,					**__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			tokenizer
				snake_case_   		=			skip_prompt
				snake_case_   		=			decode_kwargs

				# variables used in the streaming process
				snake_case_   		=			[]
				snake_case_   		=			0
				snake_case_   		=			True


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				if len(value.shape				) > 1 and value.shape[0] > 1:
					raise ValueError('TextStreamer only supports batch size 1'				)
				elif len(value.shape				) > 1:
					snake_case_   		=			value[0]

				if self.skip_prompt and self.next_tokens_are_prompt:
					snake_case_   		=			False
					return

				# Add the new token to the cache and decodes the entire thing.
				self.token_cache.extend(value.tolist()				)
				snake_case_   		=			self.tokenizer.decode(self.token_cache			,					**self.decode_kwargs				)

				# After the symbol for a new line, we flush the cache.
				if text.endswith('\n'				):
					snake_case_   		=			text[self.print_len :]
					snake_case_   		=			[]
					snake_case_   		=			0
				# If the last token is a CJK character, we print the characters.
				elif len(__UpperCamelCase				) > 0 and self._is_chinese_char(ord(text[-1]				)				):
					snake_case_   		=			text[self.print_len :]
					self.print_len += len(__UpperCamelCase				)
				# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
				# which may change with the subsequent token -- there are probably smarter ways to do this!)
				else:
					snake_case_   		=			text[self.print_len : text.rfind(' '				) + 1]
					self.print_len += len(__UpperCamelCase				)

				self.on_finalized_text(__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				if len(self.token_cache				) > 0:
					snake_case_   		=			self.tokenizer.decode(self.token_cache			,					**self.decode_kwargs				)
					snake_case_   		=			text[self.print_len :]
					snake_case_   		=			[]
					snake_case_   		=			0
				else:
					snake_case_   		=			''

				snake_case_   		=			True
				self.on_finalized_text(__UpperCamelCase			,					stream_end=__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = False				):

				"""simple docstring"""


				print(__UpperCamelCase			,					flush=__UpperCamelCase			,					end='' if not stream_end else None				)







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				if (
				    (cp >= 0X4E_00 and cp <= 0X9F_FF)
				    or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
				    or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
				    or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
				    or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
				    or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
				    or (cp >= 0XF9_00 and cp <= 0XFA_FF)
				    or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
				):  #
					return True

				return False







class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase = False			,					__UpperCamelCase = None			,					**__UpperCamelCase				):

				"""simple docstring"""


				super().__init__(__UpperCamelCase			,					__UpperCamelCase			,					**__UpperCamelCase				)
				snake_case_   		=			Queue()
				snake_case_   		=			None
				snake_case_   		=			timeout


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = False				):

				"""simple docstring"""


				self.text_queue.put(__UpperCamelCase			,					timeout=self.timeout				)
				if stream_end:
					self.text_queue.put(self.stop_signal			,					timeout=self.timeout				)


			def __iter__(     self				):

				"""simple docstring"""


				return self







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.text_queue.get(timeout=self.timeout				)
				if value == self.stop_signal:
					raise StopIteration()
				else:
					return value



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A									=  {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MraForMaskedLM',
		    'MraForMultipleChoice',
		    'MraForQuestionAnswering',
		    'MraForSequenceClassification',
		    'MraForTokenClassification',
		    'MraLayer',
		    'MraModel',
		    'MraPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mra import (
				    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MraForMaskedLM,
				    MraForMultipleChoice,
				    MraForQuestionAnswering,
				    MraForSequenceClassification,
				    MraForTokenClassification,
				    MraLayer,
				    MraModel,
				    MraPreTrainedModel,
				)
else:
		import sys

		A									=  _LazyModule(__name__, globals()['__file__'], _import_structure)
