

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_UpperCAmelCase		:       Optional[Any]						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       Union[str, Any]						    =	{
    """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
    # See all Marian models at https://huggingface.co/models?filter=marian
}







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			str	      =      '''marian'''
				__SCREAMING_SNAKE_CASE :			List[str]	      =      ['''past_key_values''']
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




				def __init__( self				,       snake_case=5_8101				,       snake_case=None				,       snake_case=1024				,       snake_case=12				,       snake_case=4096				,       snake_case=16				,       snake_case=12				,       snake_case=4096				,       snake_case=16				,       snake_case=0.0				,       snake_case=0.0				,       snake_case=True				,       snake_case=True				,       snake_case="gelu"				,       snake_case=1024				,       snake_case=0.1				,       snake_case=0.0				,       snake_case=0.0				,       snake_case=0.02				,       snake_case=5_8100				,       snake_case=False				,       snake_case=5_8100				,       snake_case=0				,       snake_case=0				,       snake_case=True				,       **snake_case				,       ):
											snake_case_          =   vocab_size
											snake_case_          =   decoder_vocab_size or vocab_size
											snake_case_          =   max_position_embeddings
											snake_case_          =   d_model
											snake_case_          =   encoder_ffn_dim
											snake_case_          =   encoder_layers
											snake_case_          =   encoder_attention_heads
											snake_case_          =   decoder_ffn_dim
											snake_case_          =   decoder_layers
											snake_case_          =   decoder_attention_heads
											snake_case_          =   dropout
											snake_case_          =   attention_dropout
											snake_case_          =   activation_dropout
											snake_case_          =   activation_function
											snake_case_          =   init_std
											snake_case_          =   encoder_layerdrop
											snake_case_          =   decoder_layerdrop
											snake_case_          =   use_cache
											snake_case_          =   encoder_layers
											snake_case_          =   scale_embedding  # scale factor will be sqrt(d_model) if True
											snake_case_          =   share_encoder_decoder_embeddings
											super().__init__(
											    pad_token_id=snake_case				,       eos_token_id=snake_case				,       is_encoder_decoder=snake_case				,       decoder_start_token_id=snake_case				,       forced_eos_token_id=snake_case				,       **snake_case				,       )







class 			lowercase    (	lowercase_      ):




				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
				def    a							( self	):
											if self.task in ["default", "seq2seq-lm"]:
																		snake_case_          =   OrderedDict(
																		    [
																		        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																		        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																		    ]	)

																		if self.use_past:
																									snake_case_          =   {0: 'batch'}
																									snake_case_          =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
																		else:
																									snake_case_          =   {0: 'batch', 1: 'decoder_sequence'}
																									snake_case_          =   {0: 'batch', 1: 'decoder_sequence'}

																		if self.use_past:
																									self.fill_with_past_key_values_(snake_case				,       direction='inputs'	)
											elif self.task == "causal-lm":
																		# TODO: figure this case out.
																		snake_case_          =   OrderedDict(
																		    [
																		        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																		        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																		    ]	)
																		if self.use_past:
																									snake_case_    ,   snake_case_          =   self.num_layers
																									for i in range(snake_case	):
																																snake_case_          =   {0: 'batch', 2: 'past_sequence + sequence'}
																																snake_case_          =   {0: 'batch', 2: 'past_sequence + sequence'}
											else:
																		snake_case_          =   OrderedDict(
																		    [
																		        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																		        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																		        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
																		        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
																		    ]	)

											return common_inputs




				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
				def    a							( self	):
											if self.task in ["default", "seq2seq-lm"]:
																		snake_case_          =   super().outputs
											else:
																		snake_case_          =   super(snake_case				,       self	).outputs
																		if self.use_past:
																									snake_case_    ,   snake_case_          =   self.num_layers
																									for i in range(snake_case	):
																																snake_case_          =   {0: 'batch', 2: 'past_sequence + sequence'}
																																snake_case_          =   {0: 'batch', 2: 'past_sequence + sequence'}
											return common_outputs




				def    a							( self				,       snake_case				,       snake_case = -1				,       snake_case = -1				,       snake_case = False				,       snake_case = None				,       ):
											snake_case_          =   self._generate_dummy_inputs_for_encoder_and_decoder(
											    snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case	)

											# Generate decoder inputs
											snake_case_          =   seq_length if not self.use_past else 1
											snake_case_          =   self._generate_dummy_inputs_for_encoder_and_decoder(
											    snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case	)
											snake_case_          =   {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
											snake_case_          =   dict(**snake_case				,       **snake_case	)

											if self.use_past:
																		if not is_torch_available():
																									raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'	)
																		else:
																									import torch
																		snake_case_    ,   snake_case_          =   common_inputs['input_ids'].shape
																		snake_case_          =   common_inputs['decoder_input_ids'].shape[1]
																		snake_case_    ,   snake_case_          =   self.num_attention_heads
																		snake_case_          =   (
																		    batch,
																		    num_encoder_attention_heads,
																		    encoder_seq_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)
																		snake_case_          =   decoder_seq_length + 3
																		snake_case_          =   (
																		    batch,
																		    num_decoder_attention_heads,
																		    decoder_past_length,
																		    self._config.hidden_size // num_decoder_attention_heads,
																		)

																		snake_case_          =   torch.cat(
																		    [common_inputs['decoder_attention_mask'], torch.ones(snake_case				,       snake_case	)]				,       dim=1	)

																		snake_case_          =   []
																		# If the number of encoder and decoder layers are present in the model configuration, both are considered
																		snake_case_    ,   snake_case_          =   self.num_layers
																		snake_case_          =   min(snake_case				,       snake_case	)
																		snake_case_          =   max(snake_case				,       snake_case	) - min_num_layers
																		snake_case_          =   'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

																		for _ in range(snake_case	):
																									common_inputs["past_key_values"].append(
																									    (
																									        torch.zeros(snake_case	),
																									        torch.zeros(snake_case	),
																									        torch.zeros(snake_case	),
																									        torch.zeros(snake_case	),
																									    )	)
																		# TODO: test this.
																		snake_case_          =   encoder_shape if remaining_side_name == 'encoder' else decoder_shape
																		for _ in range(snake_case				,       snake_case	):
																									common_inputs["past_key_values"].append((torch.zeros(snake_case	), torch.zeros(snake_case	))	)
											return common_inputs




				def    a							( self				,       snake_case				,       snake_case = -1				,       snake_case = -1				,       snake_case = False				,       snake_case = None				,       ):
											snake_case_          =   self._generate_dummy_inputs_for_encoder_and_decoder(
											    snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case	)

											if self.use_past:
																		if not is_torch_available():
																									raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'	)
																		else:
																									import torch
																		snake_case_    ,   snake_case_          =   common_inputs['input_ids'].shape
																		# Not using the same length for past_key_values
																		snake_case_          =   seqlen + 2
																		snake_case_    ,   snake_case_          =   self.num_layers
																		snake_case_    ,   snake_case_          =   self.num_attention_heads
																		snake_case_          =   (
																		    batch,
																		    num_encoder_attention_heads,
																		    past_key_values_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)

																		snake_case_          =   common_inputs['attention_mask'].dtype
																		snake_case_          =   torch.cat(
																		    [common_inputs['attention_mask'], torch.ones(snake_case				,       snake_case				,       dtype=snake_case	)]				,       dim=1	)
																		snake_case_          =   [
																		    (torch.zeros(snake_case	), torch.zeros(snake_case	)) for _ in range(snake_case	)
																		]
											return common_inputs




				def    a							( self				,       snake_case				,       snake_case = -1				,       snake_case = -1				,       snake_case = False				,       snake_case = None				,       ):
											# Copied from OnnxConfig.generate_dummy_inputs
											# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
											# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
											snake_case_          =   compute_effective_axis_dimension(
											    snake_case				,       fixed_dimension=OnnxConfig.default_fixed_batch				,       num_token_to_add=0	)

											# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
											snake_case_          =   tokenizer.num_special_tokens_to_add(snake_case	)
											snake_case_          =   compute_effective_axis_dimension(
											    snake_case				,       fixed_dimension=OnnxConfig.default_fixed_sequence				,       num_token_to_add=snake_case	)

											# Generate dummy inputs according to compute batch and sequence
											snake_case_          =   [' '.join([tokenizer.unk_token]	) * seq_length] * batch_size
											snake_case_          =   dict(tokenizer(snake_case				,       return_tensors=snake_case	)	)
											return common_inputs




				def    a							( self				,       snake_case				,       snake_case = -1				,       snake_case = -1				,       snake_case = False				,       snake_case = None				,       ):
											if self.task in ["default", "seq2seq-lm"]:
																		snake_case_          =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																		    snake_case				,       batch_size=snake_case				,       seq_length=snake_case				,       is_pair=snake_case				,       framework=snake_case	)

											else:
																		snake_case_          =   self._generate_dummy_inputs_for_causal_lm(
																		    snake_case				,       batch_size=snake_case				,       seq_length=snake_case				,       is_pair=snake_case				,       framework=snake_case	)

											return common_inputs




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case	):
											if self.task in ["default", "seq2seq-lm"]:
																		snake_case_          =   super()._flatten_past_key_values_(snake_case				,       snake_case				,       snake_case				,       snake_case	)
											else:
																		snake_case_          =   super(snake_case				,       self	)._flatten_past_key_values_(
																		    snake_case				,       snake_case				,       snake_case				,       snake_case	)




				@property
				def    a							( self	):
											return 1e-4







from __future__ import annotations

import os
from typing import Any

import requests

_UpperCAmelCase		:       int						    =	"""https://api.github.com"""

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
_UpperCAmelCase		:       Dict						    =	BASE_URL + """/user"""

# https://github.com/settings/tokens
_UpperCAmelCase		:       Optional[Any]						    =	os.environ.get("""USER_TOKEN""", """""")





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   {
							    'Authorization': F'''token {auth_token}''',
							    'Accept': 'application/vnd.github.v3+json',
							}
							return requests.get(UpperCamelCase__       ,							headers=UpperCamelCase__				).json()


if __name__ == "__main__":  # pragma: no cover
					if USER_TOKEN:
										for key, value in fetch_github_info(USER_TOKEN).items():
															print(F'''{key}: {value}''')
					else:
										raise ValueError("""'USER_TOKEN' field cannot be empty.""")




