
from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


UpperCAmelCase	      = logging.get_logger(__name__)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	if isinstance(__SCREAMING_SNAKE_CASE ,							np.ndarray							):
		return list(tensor.shape							)

	lowercase       =    tf.shape(__SCREAMING_SNAKE_CASE							)

	if tensor.shape == tf.TensorShape(__SCREAMING_SNAKE_CASE							):
		return dynamic

	lowercase       =    tensor.shape.as_list()

	return [dynamic[i] if s is None else s for i, s in enumerate(__SCREAMING_SNAKE_CASE							)]






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE = None ,							__SCREAMING_SNAKE_CASE = None							):
	return tf.nn.softmax(logits=logits + 1e-9 ,							axis=__SCREAMING_SNAKE_CASE ,							name=__SCREAMING_SNAKE_CASE							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=1e-5 ,							__SCREAMING_SNAKE_CASE=-1							):
	# This is a very simplified functional layernorm, designed to duplicate
	# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
	# models in Transformers.

	if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
		raise NotImplementedError('Only 1D weight and bias tensors are supported for now, with only a single axis.'							)

	# Get mean and variance on the axis to be normalized
	lowercase  ,					lowercase       =    tf.nn.moments(__SCREAMING_SNAKE_CASE ,							axes=[axis] ,							keepdims=__SCREAMING_SNAKE_CASE							)

	if axis != -1:
		# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
		# on every dimension except axis
		lowercase       =    [1] * inputs.shape.rank
		lowercase       =    shape_list(__SCREAMING_SNAKE_CASE							)[axis]
		lowercase       =    tf.reshape(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
		lowercase       =    tf.reshape(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

	# Compute layer normalization using the batch_normalization
	# function.
	lowercase       =    tf.nn.batch_normalization(
	    __SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							offset=__SCREAMING_SNAKE_CASE ,							scale=__SCREAMING_SNAKE_CASE ,							variance_epsilon=__SCREAMING_SNAKE_CASE ,							)
	return outputs






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=0 ,							__SCREAMING_SNAKE_CASE=-1							):
	# Replicates the behavior of torch.flatten in TF

	# If end_dim or start_dim is negative, count them from the end
	if end_dim < 0:
		end_dim += input.shape.rank
	if start_dim < 0:
		start_dim += input.shape.rank

	if start_dim == end_dim:
		return input

	lowercase       =    tf.shape(__SCREAMING_SNAKE_CASE							)
	lowercase       =    tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]							)
	lowercase       =    tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] ,							axis=0							)
	return tf.reshape(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	if not isinstance(__SCREAMING_SNAKE_CASE ,							tf.Tensor							):
		lowercase       =    tf.convert_to_tensor(__SCREAMING_SNAKE_CASE							)  # Catches stray NumPy inputs
	if encoder_attention_mask.shape.rank == 3:
		lowercase       =    encoder_attention_mask[:, None, :, :]
	if encoder_attention_mask.shape.rank == 2:
		lowercase       =    encoder_attention_mask[:, None, None, :]
	# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
	# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
	# /transformer/transformer_layers.py#L270
	# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
	# encoder_extended_attention_mask.transpose(-1, -2))
	lowercase       =    (
	    tf.cast(1 ,							encoder_attention_mask.dtype							) - encoder_extended_attention_mask
	) * encoder_extended_attention_mask.dtype.min

	return encoder_extended_attention_mask






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE = "input_ids"							):
	tf.debugging.assert_less(
	    __SCREAMING_SNAKE_CASE ,							tf.cast(__SCREAMING_SNAKE_CASE ,							dtype=tensor.dtype							) ,							message=(
	        F'''The maximum value of {tensor_name} ({tf.math.reduce_max(__SCREAMING_SNAKE_CASE							)}) must be smaller than the embedding '''
	        F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
	    ) ,							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    6_4512
	# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
	# because in that case even chunking the array would not make the saving
	# possible.
	lowercase       =    [x for x in data if len(__SCREAMING_SNAKE_CASE							) > HDF5_OBJECT_HEADER_LIMIT]

	# Expecting this to never be true.
	if bad_attributes:
		raise RuntimeError(
		    'The following attributes cannot be saved to HDF5 file because '
		    F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
		    F'''bytes: {bad_attributes}'''							)

	lowercase       =    np.asarray(__SCREAMING_SNAKE_CASE							)

	lowercase       =    1
	lowercase       =    np.array_split(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

	# This will never loop forever thanks to the test above.
	while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data							):
		num_chunks += 1
		lowercase       =    np.array_split(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

	if num_chunks > 1:
		for chunk_id, chunk_data in enumerate(__SCREAMING_SNAKE_CASE							):
			lowercase       =    chunk_data
	else:
		lowercase       =    data






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	if name in group.attrs:
		lowercase       =    [n.decode('utf8'							) if hasattr(__SCREAMING_SNAKE_CASE ,							'decode'							) else n for n in group.attrs[name]]
	else:
		lowercase       =    []
		lowercase       =    0
		while "%s%d" % (name, chunk_id) in group.attrs:
			data.extend(
			    [n.decode('utf8'							) if hasattr(__SCREAMING_SNAKE_CASE ,							'decode'							) else n for n in group.attrs['%s%d' % (name, chunk_id)]]							)
			chunk_id += 1
	return data






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):

	def _expand_single_ad_tensor(__SCREAMING_SNAKE_CASE							):
		if isinstance(__SCREAMING_SNAKE_CASE ,							tf.Tensor							) and t.shape.rank == 1:
			return tf.expand_dims(__SCREAMING_SNAKE_CASE ,							axis=-1							)
		return t

	return tf.nest.map_structure(_expand_single_ad_tensor ,							__SCREAMING_SNAKE_CASE							)



import pprint

import requests

UpperCAmelCase	      = '''https://zenquotes.io/api'''






def 							UpperCAmelCase_		(							):
	return requests.get(API_ENDPOINT_URL + '/today'							).json()






def 							UpperCAmelCase_		(							):
	return requests.get(API_ENDPOINT_URL + '/random'							).json()


if __name__ == "__main__":
	UpperCAmelCase	      = random_quotes()
	pprint.pprint(response)

